,Unnamed: 0,input_text,target_text,indexes
0,4613,Bayesian learning uses Bayes' theorem to determine the conditional probability of a hypotheses given some evidence or observations.,What is Bayesian learning in machine learning,4613
1,7729,"Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.",What does linear regression tell you,7729
2,4991,"Mentor: Well, if the line is a good fit for the data then the residual plot will be random. However, if the line is a bad fit for the data then the plot of the residuals will have a pattern.",How do you know if a residual plot is good,4991
3,7577,"Build the model on the training set and then use the test set as a holdout sample to test your trained model using the test data. Compare the predicted values with the actual values by calculating the error using measures such as the ""Mean Absolute Percent Error"" (MAPE) for example.",How can you tell if the predictive model is accurate,7577
4,9601,"The Implicit Association Test (IAT) measures the strength of associations between concepts (e.g., black people, gay people) and evaluations (e.g., good, bad) or stereotypes (e.g., athletic, clumsy). The main idea is that making a response is easier when closely related items share the same response key.",How does the implicit bias test work,9601
5,5533,The key to interpreting a hierarchical cluster analysis is to look at the point at which any given pair of cards “join together” in the tree diagram. Cards that join together sooner are more similar to each other than those that join together later.,How do you interpret a hierarchical cluster analysis,5533
6,100,"Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.",What are the different types of data sets used in ML,100
7,9534,Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics. Descriptive statistics are used to synopsize data from a sample exercising the mean or standard deviation. Inferential statistics are used when data is viewed as a subclass of a specific population.,What are the different types of statistics used in research,9534
8,8577,"A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem. — Practical recommendations for gradient-based training of deep architectures, 2012.",What should be the learning rate,8577
9,1507,"Key Differences between AI, ML, and NLP ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).",What is the difference between AI machine learning NLP and deep learning,1507
10,2675,The relative frequencies add up to 1.,Aside in a relative frequency distribution what should the relative frequencies add up to,2675
11,5981,"Quartiles let us quickly divide a set of data into four groups, making it easy to see which of the four groups a particular data point is in. For example, a professor has graded an exam from 0-100 points.",How are quartiles used in real life,5981
12,2577,"The exponential distribution is a continuous probability distribution used to model the time we need to wait before a given event occurs. It is the continuous counterpart of the geometric distribution, which is instead discrete.",Why do we use exponential distribution,2577
13,5413,"Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train.",Why do we need to do Hyperparameter tuning in neural networks,5413
14,9591,"A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model.  The rows represent the predicted values of the target variable.",What is confusion matrix in machine learning,9591
15,1585,"A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task.",What is a learning algorithm,1585
16,8021,"Basically, the test compares the fit of two models. The null hypothesis is that the smaller model is the “best” model; It is rejected when the test statistic is large. In other words, if the null hypothesis is rejected, then the larger model is a significant improvement over the smaller one.",What is the null hypothesis for likelihood ratio test,8021
17,6061,"In computational linguistics, second-order co-occurrence pointwise mutual information is a semantic similarity measure. To assess the degree of association between two given words, it uses pointwise mutual information (PMI) to sort lists of important neighbor words of the two target words from a large corpus.",How does second order pointwise mutual information information retrieval work,6061
18,1732,"In cryptography, padding is any of a number of distinct practices which all include adding data to the beginning, middle, or end of a message prior to encryption.",What is padding in encryption,1732
19,3799,Matrix factorization using the alternating least squares algorithm for collaborative filtering. Alternating least squares (ALS) is an optimization technique to solve the matrix factorization problem. This technique achieves good performance and has proven relatively easy to implement.,What is the significance of alternating least squares in collaborative filtering,3799
20,9617,"Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question ""what might possibly happen in the future?""",How does predictive modeling work,9617
21,5990,So the probability that the sample mean will be >22 is the probability that Z is > 1.6 We use the Z table to determine this: P( > 22) = P(Z > 1.6) = 0.0548.,How do you find the probability of a sample mean,5990
22,9265,"The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done. Let's have a short reminder of how RNNs look like.",What is gradient RNN,9265
23,221,"The probability that a random variable X X X takes a value in the (open or closed) interval [ a , b ] [a,b] [a,b] is given by the integral of a function called the probability density function f X ( x ) f_X(x) fX​(x): P ( a ≤ X ≤ b ) = ∫ a b f X ( x ) d x .",How do you find the probability density function of a random variable,221
24,4594,1:2611:18Suggested clip · 118 secondsMultiple Logistic Regression in SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you do a multiple logistic regression in SPSS,4594
25,5887,"In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.",Why is stratified sampling used,5887
26,5844,How to Use GA for Optimization Problems?Generate the initial population randomly.Select the initial solution with the best fitness values.Recombine the selected solutions using mutation and crossover operators.Insert offspring into the population.More items,How is genetic algorithm used in neural networks,5844
27,4025,"Correlation analysis explores the association between two or more variables and makes inferences about the strength of the relationship.  Technically, association refers to any relationship between two variables, whereas correlation is often used to refer only to a linear relationship between two variables.",What is Association and correlation in data mining,4025
28,601,"Under the batch processing model, a set of data is collected over time, then fed into an analytics system. In other words, you collect a batch of information, then send it in for processing. Under the streaming model, data is fed into analytics tools piece-by-piece. The processing is usually done in real time.",What are the differences between batch processing and stream processing systems,601
29,5060,"In systematic sampling, the list of elements is ""counted off"". That is, every kth element is taken.  Stratified sampling also divides the population into groups called strata. However, this time it is by some characteristic, not geographically.",What is the difference between stratified sampling and systematic sampling,5060
30,10467,Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time.  VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. VAR models are often used in economics and the natural sciences.,What is VAR model in econometrics,10467
31,6451,"Topic modelling refers to the task of identifying topics that best describes a set of documents.  And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics.",How does LDA topic modeling work,6451
32,1565,"Deep learning (sometimes known as deep structured learning) is a subset of machine learning, where machines employ artificial neural networks to process information. Inspired by biological nodes in the human body, deep learning helps computers to quickly recognize and process images and speech.",What is deep learning and how is it useful,1565
33,7633,Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. Gradient descent is simply used to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible.,What is the use of gradient descent in machine learning,7633
34,9345,"A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers.",What is ResNet in deep learning,9345
35,9802,7 Answers. Gradient is covariant!  The components of a vector contravariant because they transform in the inverse (i.e. contra) way of the vector basis. It is customary to denote these components with an upper index.,Why is gradient covariant,9802
36,4670,"OCR converts images of typed or handwritten text into machine-encoded text. The major steps in image recognition process are gather and organize data, build a predictive model and use it to recognize images.",How does image recognition AI work,4670
37,8169,"more  A symbol for a value we don't know yet. It is usually a letter like x or y. Example: in x + 2 = 6, x is the variable.",What is variable and example,8169
38,4481,The general algorithm is The Backpropagation algorithm is suitable for the feed forward neural network on fixed sized input-output pairs. The Backpropagation Through Time is the application of Backpropagation training algorithm which is applied to the sequence data like the time series.,What is the difference between backpropagation algorithm and Backpropagation through time Bptt algorithm,4481
39,10327,The range is influenced too much by extreme values.,Which difficulty of range as a measure of variability is overcome by interquartile range,10327
40,2536,"Stochastic processes appear in many different fields, including the physical sciences such as biology, chemistry, ecology, neuroscience, and physics as well as technology and engineering fields such as image processing, signal processing, information theory, computer science,, cryptography and telecommunications.",Where is stochastic processes used,2536
41,10842,Define Population Distribution; and sketch a graph: The population distribution gives the values of the variable for all the individuals in the population.  The sampling distribution shows the statistic values from all the possible samples of the same size from the population. It is a distribution of the statistic.,What is the difference between a sampling distribution and a population distribution,10842
42,3538,"Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function.  But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent.",Is gradient descent an optimization algorithm,3538
43,8926,"Iterable is an object, which one can iterate over. It generates an Iterator when passed to iter() method. Iterator is an object, which is used to iterate over an iterable object using __next__() method.  Note that every iterator is also an iterable, but not every iterable is an iterator.",What are iterators and Iterables in Python,8926
44,8782,"Gibbs Sampling is based on sampling from condi- tional distributions of the variables of the posterior.  For LDA, we are interested in the latent document-topic portions θd, the topic-word distributions φ(z), and the topic index assignments for each word zi.",What is Gibbs sampling in LDA,8782
45,10222,"This cross-sectional sample provides us with a snapshot of that population, at that one point in time.  Panel data differs from pooled cross-sectional data across time, because it deals with the observations on the same subjects in different times whereas the latter observes different subjects in different time periods.",What is the difference between cross sectional data and panel data,10222
46,6204,Popular algorithms that can be used for binary classification include:Logistic Regression.k-Nearest Neighbors.Decision Trees.Support Vector Machine.Naive Bayes.,Which algorithms are used to do a binary classification,6204
47,3497,"Textual entailment (TE) in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively.",How can you explain the concept of Recognizing Textual Entailment in NLP,3497
48,1794,"An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.",What is the purpose of an F test,1794
49,3977,"Example: One nanogram of Plutonium-239 will have an average of 2.3 radioactive decays per second, and the number of decays will follow a Poisson distribution.",What is the real life example of Poisson distribution,3977
50,2551,"Dimensionality Reduction and PCA. Dimensionality reduction refers to reducing the number of input variables for a dataset. If your data is represented using rows and columns, such as in a spreadsheet, then the input variables are the columns that are fed as input to a model to predict the target variable.",What is PCA dimensionality reduction,2551
51,4030,"While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. In reporting and interpreting studies, both the substantive significance (effect size) and statistical significance (P value) are essential results to be reported.",Is AP value an effect size,4030
52,9294,"In probability theory, an event is an outcome or defined collection of outcomes of a random experiment. Since the collection of all possible outcomes to a random experiment is called the sample space, another definiton of event is any subset of a sample space.",What is the definition of an event in statistics,9294
53,4005,"Linear filters process time-varying input signals to produce output signals, subject to the constraint of linearity.  Since linear time-invariant filters can be completely characterized by their response to sinusoids of different frequencies (their frequency response), they are sometimes known as frequency filters.",What makes a filter linear,4005
54,10629,"Stationarity. A common assumption in many time series techniques is that the data are stationary. A stationary process has the property that the mean, variance and autocorrelation structure do not change over time.",What is stationarity in time series analysis,10629
55,1114,"The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. In the 16th century, mathematician Gerolama Cardano recognized the Law of Large Numbers but never proved it.",What does the law of large numbers say,1114
56,10921,The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking.,Which method is used in decision tree algorithm,10921
57,3469,"A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. A classification model attempts to draw some conclusion from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.",What are classification problems in machine learning,3469
58,4627,"To review, the Forget gate decides what is relevant to keep from prior steps. The input gate decides what information is relevant to add from the current step. The output gate determines what the next hidden state should be.",Which of the following gates in Lstm decides on keeping relevant features from the current input,4627
59,4531,"A mean can be determined for grouped data, or data that is placed in intervals.  The sum of the products divided by the total number of values will be the value of the mean.",What is the mean for grouped data,4531
60,3592,"Recurrent Neural Networks (RNNs) are a form of machine learning algorithm that are ideal for sequential data such as text, time series, financial data, speech, audio, video among others.",Does recurrent neural networks are best suited for text processing,3592
61,6265,Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. Please note: The purpose of this page is to show how to use various data analysis commands.,When should I use robust regression,9231
62,8188,"Meta-learning, also known as “learning to learn”, intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples.  Humans, in contrast, learn new concepts and skills much faster and more efficiently.",How does meta learning work,8188
63,9573,Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x). Classification: when the function being learned is discrete. Regression: when the function being learned is continuous.,What is inductive learning in machine learning,9573
64,1454,An activation function is defined by and defines the output of a neuron in terms of its input (aka induced local field) . There are three types of activation functions. Threshhold function an example of which is. This function is also termed the Heaviside function. Piecewise Linear.,What is activation function and its types,1454
65,51,"How to Detect Omitted Variable Bias and Identify Confounding Variables. You saw one method of detecting omitted variable bias in this post. If you include different combinations of independent variables in the model, and you see the coefficients changing, you're watching omitted variable bias in action!",How do you identify omitted variable bias,51
66,1682,"It can be seen that the function of the loss of quality is a U-shaped curve, which is determined by the following simple quadratic function: L(x)= Quality loss function. x = Value of the quality characteristic (observed). N = Nominal value of the quality characteristic (Target value – target).",How is the target value of a Taguchi loss function identified,1682
67,1971,"Anything central is in the middle of something — or essential to it. Central things are fundamental and important. Think about the center of a circle: it's right in the middle, equidistant from all sides. Similarly, anything central is in the middle of something.",What does the word central mean,1971
68,3069,"In contrast to the non-stationary process that has a variable variance and a mean that does not remain near, or returns to a long-run mean over time, the stationary process reverts around a constant long-term mean and has a constant variance independent of time.",What is the difference between stationary and non stationary time series,3069
69,9889,"The Google Goggles app is an image-recognition mobile app that uses visual search technology to identify objects through a mobile device's camera. Users can take a photo of a physical object, and Google searches and retrieves information about the image.",How can I identify an object in a picture,9889
70,4343,"The kurtosis of any univariate normal distribution is 3. It is common to compare the kurtosis of a distribution to this value. Distributions with kurtosis less than 3 are said to be platykurtic, although this does not imply the distribution is ""flat-topped"" as is sometimes stated.",What is the kurtosis of a normal distribution,4343
71,541,"Rule-based systems process data and output information, but they also process rules and make decisions.  Knowledge-based systems also process data and rules to output information and make decisions. In addition, they also process expert knowledge to output answers, recommendations, and expert advice.",What is the difference between a rule based system and a knowledge based system,541
72,101,"It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction.",What is loss function in machine learning,101
73,5132,How to train your Deep Neural NetworkTraining data.  Choose appropriate activation functions.  Number of Hidden Units and Layers.  Weight Initialization.  Learning Rates.  Hyperparameter Tuning: Shun Grid Search - Embrace Random Search.  Learning Methods.  Keep dimensions of weights in the exponential power of 2.More items•,How do I train deep neural networks,5132
74,10452,Feature selection methods are intended to reduce the number of input variables to those that are believed to be most useful to a model in order to predict the target variable. Feature selection is primarily focused on removing non-informative or redundant predictors from the model.,What are feature selection techniques in machine learning,10452
75,3102,"In Regression Clustering (RC), K (>1) regression functions are applied to the dataset simultaneously which guide the clustering of the dataset into K subsets each with a simpler distribution matching its guiding function. Each function is regressed on its own subset of data with a much smaller residue error.",What is clustering in regression,3102
76,5611,"17. Deep Convolutional Network (DCN): Convolutional Neural Networks are neural networks used primarily for classification of images, clustering of images and object recognition.",What is neural network classification,5611
77,3149,The function scipy. linalg. eig computes eigenvalues and eigenvectors of a square matrix .,What is the function to get both eigenvalues and eigenvectors of a matrix,3149
78,2770,"The confidence of an association rule is a percentage value that shows how frequently the rule head occurs among all the groups containing the rule body.  Thus, the confidence of a rule is the percentage equivalent of m/n, where the values are: m. The number of groups containing the joined rule head and rule body.",What is confidence in association rule,2770
79,3449,"A simple random sample is used to represent the entire data population and. randomly selects individuals from the population without any other consideration. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics.",What is the difference between random sampling and stratified sampling,3449
80,9393,"Parametric tests assume a normal distribution of values, or a “bell-shaped curve.” For example, height is roughly a normal distribution in that if you were to graph height from a group of people, one would see a typical bell-shaped curve.",What is an example of parametric statistics,9393
81,5013,Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.,How do we know whether a model is overfitting,5013
82,8113,"Morpheus: If real is what you can feel, smell, taste and see, then 'real' is simply electrical signals interpreted by your brain.",What is the Matrix Morpheus quote,8113
83,8752,"A squashing function is essentially defined as a function that squashes the input to one of the ends of a small interval. In Neural Networks, these can be used at nodes in a hidden layer to squash the input. This introduces non-linearity to the NN and allows the NN to be effective.",Why squashing function is important in neural network,8752
84,5211,"Definition 1. A statistic d is called an unbiased estimator for a function of the parameter g(θ) provided that for every choice of θ, Eθd(X) = g(θ). Any estimator that not unbiased is called biased.  Note that the mean square error for an unbiased estimator is its variance.",What is an unbiased estimator of variance,5211
85,7300,"Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme case.",What does kurtosis indicate,7300
86,4668,"In neural networks, a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network.",What is the purpose of hidden layer in neural network,4668
87,5527,"AI means getting a computer to mimic human behavior in some way.  Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.",How are the terms artificial intelligence machine learning and deep learning related,5527
88,7939,"Loss is the penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater.",What is the loss in machine learning,7939
89,8668,"Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design.",How can algorithms be biased,8668
90,9004,"Description. VGG-19 is a convolutional neural network that is 19 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.",What is Vgg in deep learning,9004
91,10687,"The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of those distributions.",Which is better variance or standard deviation,10687
92,1828,K-fold cross-validationRandomly split the data set into k-subsets (or k-fold) (for example 5 subsets)Reserve one subset and train the model on all other subsets.Test the model on the reserved subset and record the prediction error.Repeat this process until each of the k subsets has served as the test set.More items•,How do you do k fold cross validation in R,1828
93,2888,- YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you find the median for continuous data,2888
94,8428,"How to calculate percentileRank the values in the data set in order from smallest to largest.Multiply k (percent) by n (total number of values in the data set).  If the index is not a round number, round it up (or down, if it's closer to the lower number) to the nearest whole number.Use your ranked data set to find your percentile.",How is percentile calculated,8428
95,4210,The general linear model requires that the response variable follows the normal distribution whilst the generalized linear model is an extension of the general linear model that allows the specification of models whose response variable follows different distributions.,What is the difference between linear model and generalized linear model,4210
96,5553,"If there are c or less defective items in the sample, the lot is accepted. If there are more than c defective items in the sample, the lot is rejected. In other words, the acceptance or rejection of the lot depends on the inspection results of a single sample.",Under what conditions is lot for lot acceptance sampling not accepted,5553
97,10703,"The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.",How does neural network determine the number of hidden layers,10703
98,10726,"Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal distribution. For example, if a random process is modelled as a Gaussian process, the distributions of various derived quantities can be obtained explicitly.",What are Gaussian processes used for,10726
99,6954,"August 2017) (Learn how and when to remove this template message) In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.",What is Latent Dirichlet Allocation in machine learning,6954
100,8269,"Six quick tips to improve your regression modelingA.1. Fit many models.  A.2. Do a little work to make your computations faster and more reliable.  A.3. Graphing the relevant and not the irrelevant.  A.4. Transformations.  A.5. Consider all coefficients as potentially varying.  A.6. Estimate causal inferences in a targeted way, not as a byproduct of a large regression.",How can statistical models be improved,8269
101,332,"The Binomial Theorem is a quick way (okay, it's a less slow way) of expanding (or multiplying out) a binomial expression that has been raised to some (generally inconveniently large) power. For instance, the expression (3x – 2)10 would be very painful to multiply out by hand.",How do you use the binomial theorem,332
102,4145,"Use of AI in Following Things/Fields/Areas:Virtual Assistant or Chatbots.Agriculture and Farming.Autonomous Flying.Retail, Shopping and Fashion.Security and Surveillance.Sports Analytics and Activities.Manufacturing and Production.Live Stock and Inventory Management.More items•",Where is artificial intelligence used,4145
103,3340,"In statistics, the t-statistic is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error.  For example, the T-statistic is used in estimating the population mean from a sampling distribution of sample means if the population standard deviation is unknown.",What does the t statistic mean,3340
104,2054,"To solve the problem using logistic regression we take two parameters w, which is n dimensional vector and b which is a real number. The logistic regression model to solve this is : Equation for Logistic Regression. We apply sigmoid function so that we contain the result of ŷ between 0 and 1 (probability value).",What is W and B in logistic regression,2054
105,9305,"1.1 The Role of Logic in Artificial Intelligence Logic, for instance, can provide a specification for a programming language by characterizing a mapping from programs to the computations that they license.",What is the role of logic in artificial intelligence,9305
106,5116,"Every probability pi is a number between 0 and 1, and the sum of all the probabilities is equal to 1. Examples of discrete random variables include: The number of eggs that a hen lays in a given day (it can't be 2.3) The number of people going to a given soccer match.",What is a discrete random variable What are some examples,5116
107,3077,"The mean is also to the left of the peak. A right-skewed distribution has a long right tail.  Next, you'll see a fair amount of negatively skewed distributions. For example, household income in the U.S. is negatively skewed with a very long left tail.",What is an example of skewed data,3077
108,6434,"A linear threshold unit is a simple artificial neuron whose output is its thresholded total net input. That is, an LTU with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T.",What is linear threshold unit,6434
109,7641,The Pearson's correlation coefficient is calculated as the covariance of the two variables divided by the product of the standard deviation of each data sample. It is the normalization of the covariance between the two variables to give an interpretable score.,How do you find the correlation between many variables,7641
110,1398,"Among the learning algorithms, one of the most popular and easiest to understand is the decision tree induction. The popularity of this method is related to three nice characteristics: interpretability, efficiency, and flexibility. Decision tree can be used for both classification and regression kind of problem.",What are the important characteristics of decision tree induction algorithm,1398
111,3915,"RBMs were invented by Geoffrey Hinton and can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. RBMs are a special class of Boltzmann Machines and they are restricted in terms of the connections between the visible and the hidden units.",What are restricted Boltzmann machines used for,3915
112,10985,"In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network.",What is Perceptron in neural network,10985
113,2082,"Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image.",What do you mean by image processing,2082
114,9650,"Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable.",What is regression effect in data collection,9650
115,9890,"You description is confusing, but it is totally possible to have test error both lower and higher than training error. A lower training error is expected when a method easily overfits to the training data, yet, poorly generalizes.",Is it possible to have a higher train error than a test error in machine learning,9890
116,6898,Aspin-Welch t-test,What test should you use to determine the equality of the two sample means when the population standard deviation is unknown,6898
117,5147,"Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.  It is often used in signal processing for analyzing functions or series of values, such as time domain signals.",What is autocorrelation in signal and system,5147
118,10283,"Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set.",How do you explain variance,10283
119,286,"There are 3 main ways of describing the intensity of an activity – vigorous, moderate, and gentle. Vigorous activities tend to make you “huff and puff”.",How do you describe your activity level,286
120,10832,"If X and Y are normed vector spaces (a special type of TVS), then L is bounded if and only if there exists some M ≥ 0 such that for all x in X, ||Lx||Y ≤ M ||x||X. The smallest such M, denoted by ||L||, is called the operator norm of L.",How do you show an operator is bounded,10832
121,6306,"A bell curve is a common type of distribution for a variable, also known as the normal distribution. The term ""bell curve"" originates from the fact that the graph used to depict a normal distribution consists of a symmetrical bell-shaped curve.",What is the distribution of a bell curve,6306
122,5755,"The terms cost and loss functions almost refer to the same meaning. But, loss function mainly applies for a single training set as compared to the cost function which deals with a penalty for a number of training sets or the complete batch.  The cost function is calculated as an average of loss functions.",What is the difference between a cost function and a loss function in machine learning,5755
123,1831,"Max pooling is a sample-based discretization process. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc.), reducing its dimensionality and allowing for assumptions to be made about features contained in the sub-regions binned.",What is the purpose of Max pooling,1831
124,7727,"Properties. Unlike the classical conditional entropy, the conditional quantum entropy can be negative.  Positive conditional entropy of a state thus means the state cannot reach even the classical limit, while the negative conditional entropy provides for additional information.",Can conditional entropy negative,7727
125,4433,A quartile is a statistical term that describes a division of observations into four defined intervals based on the values of the data and how they compare to the entire set of observations.,What is the definition of quartile in statistics,4433
126,10280,The nominator is the joint probability and the denominator is the probability of the given outcome.  This is the conditional probability: P(A∣B)=P(A∩B)P(B) This is the Bayes' rule: P(A∣B)=P(B|A)∗P(A)P(B).,What is the difference between conditional probability and Bayes Theorem,10280
127,10160,The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest. The mode is the number that occurs most often in a data set.,What is the mean value in statistics,10160
128,1822,"Word embeddings are created using a neural network with one input layer, one hidden layer and one output layer. The computer does not understand that the words king, prince and man are closer together in a semantic sense than the words queen, princess, and daughter. All it sees are encoded characters to binary.",How are word Embeddings generated,1822
129,2964,"These are the steps we are going to do:Make a stupid model as an example, train and store it.Fetch the variables you need from your stored model.Build the tensor info from them.Create the model signature.Create and save a model builder.Download a Docker image with TensorFlow serving already compile on it.More items•",How do you deploy TensorFlow in production,2964
130,3702,"Face recognition systems use computer algorithms to pick out specific, distinctive details about a person's face. These details, such as distance between the eyes or shape of the chin, are then converted into a mathematical representation and compared to data on other faces collected in a face recognition database.",How does the facial recognition technology work,3702
131,5005,"Logarithmic scales reduce wide-ranging quantities to tiny scopes. For example, the decibel (dB) is a unit used to express ratio as logarithms, mostly for signal power and amplitude (of which sound pressure is a common example). In chemistry, pH is a logarithmic measure for the acidity of an aqueous solution.",What are logarithms used for,5005
132,8289,"While the previous study (Wu et al., 2015) suggests that ingroup derogation is a specialized mechanism which disregards explicit disease-relevant information mediated by outgroup members, a different pattern was observed in Experiment 2.",What is ingroup derogation,8289
133,8622,"2:1422:33Suggested clip · 114 secondsRegression Trees, Clearly Explained!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip",How do you interpret a regression tree,8622
134,3476,"Definition. A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied.",Is a randomized controlled trial an experimental design,3476
135,3969,"P ∧ Q means P and Q. P ∨ Q means P or Q. An argument is valid if the following conditional holds: If all the premises are true, the conclusion must be true.  So, when you attempt to write a valid argument, you should try to write out what the logical structure of the argument is by symbolizing it.",What does P ∧ Q mean,3969
136,9933,Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain.,What is regularization in deep learning,9933
137,9538,"The 'd' means a Δ in the limit approaching zero. Basically the slope is approximately Δy/Δx but if you let Δx approach zero, you reach the exactly slope which is then dy/dx.",What does the D stand for in differentiation,9538
138,2009,A statistical hypothesis is an explanation about the relationship between data populations that is interpreted probabilistically.  A machine learning hypothesis is a candidate model that approximates a target function for mapping inputs to outputs.,What is specific hypothesis in machine learning,2009
139,1229,The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement.  It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data.,What is bootstrap in machine learning,5158
140,3895,"Moments About the MeanFirst, calculate the mean of the values.Next, subtract this mean from each value.Then raise each of these differences to the sth power.Now add the numbers from step #3 together.Finally, divide this sum by the number of values we started with.",How do you calculate moments in statistics,3895
141,1699,"Covariance indicates the relationship of two variables whenever one variable changes. If an increase in one variable results in an increase in the other variable, both variables are said to have a positive covariance.  Both variables move together in the same direction when they change.",What does Covariance indicate,1699
142,4803,"Clustering is considered unsupervised learning, because there's no labeled target variable in clustering. Clustering algorithms try to, well, cluster data points into similar groups (or… clusters) based on different characteristics of the data.",Is clustering supervised or unsupervised How do you classify it,4803
143,2833,"As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an effective tool in real-value function estimation.",What is SVR regression,2833
144,2850,"The linear Discriminant analysis estimates the probability that a new set of inputs belongs to every class.  LDA uses Bayes' Theorem to estimate the probabilities. If the output class is (k) and the input is (x), here is how Bayes' theorem works to estimate the probability that the data belongs to each class.",How does linear discriminant analysis work,2850
145,7002,"In neural networks, each neuron receives input from some number of locations in the previous layer. In a fully connected layer, each neuron receives input from every element of the previous layer. In a convolutional layer, neurons receive input from only a restricted subarea of the previous layer.",What is the difference between a neural network and a convolutional network,7002
146,6595,"A single layer perceptron (SLP) is a feed-forward network based on a threshold transfer function. SLP is the simplest type of artificial neural networks and can only classify linearly separable cases with a binary target (1 , 0).",What is a single layer Perceptron,6595
147,831,"Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results.",Why is Multicollinearity a problem in linear regression select the correct option,831
148,9481,"Question: 1. When A Value Of Y Is Calculated Using The Regression Equation (Y_hat), It Is Called: -the Fitted Value -the Estimated Value -the Predicted Value -all Of The Above 2.",When a value of y is calculated using the regression equation it is called,9481
149,7374,"The fact is almost all big data sets, generated by systems powered by ML/AI based models, are known to be biased. However, most ML modelers are not aware of these biases and even if they are, they do not know what to do about it.  Most (almost all) big datasets generated by ML powered systems are biased.",Is all data biased,7374
150,105,"Q-learning is an off policy reinforcement learning algorithm that seeks to find the best action to take given the current state. It's considered off-policy because the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn't needed.",How does Q learning work,105
151,6481,"Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement.",How do you interpret Cohen's kappa,6481
152,6262,"Accuracy is well defined for any number of classes, so if you use this, a single plot should suffice. Precision and recall, however, are defined only for binary problems.",How many learning curves should I plot for a multi class logistic regression classifier,6262
153,834,"In order to calculate the sample size needed for your survey or experiment, you will need to follow these steps: Determine the total population size.Complete the calculation.Determine the total population size.  Decide on a margin of error.  Choose a confidence level.  Pick a standard of deviation.  Complete the calculation.",How do you calculate sample size needed,834
154,238,"The main advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. For example, given many pictures of cats and dogs, it can learn the key features for each class by itself.",What is the advantage of CNN,238
155,928,"When you reject the null hypothesis with a t-test, you are saying that the means are statistically different. The difference is meaningful. Chi Square:  When you reject the null hypothesis with a Chi-Square, you are saying that there is a relationship between the two variables.",What is the difference between at test and a chi square,928
156,7416,Classification SVM Type 1 (also known as C-SVM classification); Classification SVM Type 2 (also known as nu-SVM classification); Regression SVM Type 1 (also known as epsilon-SVM regression); Regression SVM Type 2 (also known as nu-SVM regression).,What are the two classification methods that SVM can handle,7416
157,10165,"For values of x > 0, the gamma function is defined using an integral formula as Γ(x) = Integral on the interval [0, ∞ ] of ∫ 0∞t x −1 e−t dt. The probability density function for the gamma distribution is given by. The mean of the gamma distribution is αβ and the variance (square of the standard deviation) is αβ2.",What is the mean of gamma distribution,10165
158,6812,"The most effective tool found for the task for image recognition is a deep neural network, specifically a Convolutional Neural Network (CNN).",Which algorithm is best for image processing,6812
159,3113,A Sampling unit is one of the units selected for the purpose of sampling. Each unit being regarded as individual and indivisible when the selection is made. CONTEXT: Many times the Sampling frame and the Sampling unit are derived from Administrative data.,What is sampling unit and sampling frame,3113
160,9618,Probability limits are used when the parameter is considered as the realization of a random variable with given prior distribution.,What is a probability limit,9618
161,2243,"Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability.",Which algorithm is used in artificial intelligence,2243
162,625,"The general application of the matrix norm is the derivative form of finding proof in terms of interplay and tandem of vectorial normalized formats to whom are extended.. It can be used in tandem with Graphical processing, image processing, all kinds of algorithmics in terms of calculations and derivatives..",What is the application of matrix norm,625
163,10192,Software Testing MethodologiesFunctional vs. Non-functional Testing.  Unit Testing. Unit testing is the first level of testing and is often performed by the developers themselves.  Integration Testing.  System Testing.  Acceptance Testing.  Performance Testing.  Security Testing.  Usability Testing.More items,What are different testing techniques,10192
164,8163,"A machine learning task is the type of prediction or inference being made, based on the problem or question that is being asked, and the available data. For example, the classification task assigns data to categories, and the clustering task groups data according to similarity.",What are the tasks in machine learning,8163
165,8890,The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting). The variance is an error from sensitivity to small fluctuations in the training set.,What is bias in machine learning algorithms,8890
166,1231,hamming distance,Which distance metric do we use in Knn for categorical variables,1231
167,1298,"The modern mathematical theory of probability has its roots in attempts to analyze games of chance by Gerolamo Cardano in the sixteenth century, and by Pierre de Fermat and Blaise Pascal in the seventeenth century (for example the ""problem of points"").",Who developed the theory of probability,1298
168,8430,". Thus logit regression is simply the GLM when describing it in terms of its link function, and logistic regression describes the GLM in terms of its activation function.",Whats the difference between logit and logistic regression 1,8430
169,10810,"A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”).  It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.",What do box plots tell you,10810
170,4993,: one half of the difference obtained by subtracting the first quartile from the third quartile in a frequency distribution.,What is quartile deviation,4993
171,4043,A conditional probability estimate is a probability estimate that we make given or assuming the occurrence of some other event. In this case we might start with an estimate that the probability of rain is 30% and then make a conditional probability estimate that the probability of rain given a cloudy sky is 65%.,Is a conditional probability estimate,4043
172,10300,"Accuracy is the percentage of correctly classifies instances out of all instances.  Kappa or Cohen's Kappa is like classification accuracy, except that it is normalized at the baseline of random chance on your dataset.",What is accuracy and Kappa,10300
173,3987,"Hyperparameter optimization in machine learning intends to find the hyperparameters of a given machine learning algorithm that deliver the best performance as measured on a validation set. Hyperparameters, in contrast to model parameters, are set by the machine learning engineer before training.",What is Hyperparameter optimization in deep learning,3987
174,4556,"Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points.",Why do we use standard deviation over variance,4556
175,9767,"Nonparametric tests have the following limitations: Nonparametric tests are usually less powerful than corresponding parametric test when the normality assumption holds. Thus, you are less likely to reject the null hypothesis when it is false if the data comes from the normal distribution.",What are the main limitations of non parametric test,9767
176,6320,"Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.",How do you explain logistic regression model,6320
177,6937,"You can tell if two random variables are independent by looking at their individual probabilities. If those probabilities don't change when the events meet, then those variables are independent. Another way of saying this is that if the two variables are correlated, then they are not independent.",How do you prove a random variable is independent,6937
178,3037,"To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an ""artificial neural network” that can learn and make intelligent decisions on its own.",What is the relationship between machine learning and deep learning,3037
179,10286,Mean of General discrete uniform distribution The expected value of discrete uniform random variable is E ( X ) = a + b 2 .,What are the expected moments of a uniform discrete distribution,10286
180,54,"Moments help in finding AM, standard deviation and variance of the population directly, and they help in knowing the graphic shapes of the population. We can call moments as the constants used in finding the graphic shape, as the graphic shape of the population also help a lot in characterizing a population.",What is the use of moments in statistics,54
181,7768,Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners.  Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners.,How do ensemble methods work and why are they superior to individual models,7768
182,1352,Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a probability distribution based on constructing a Markov chain that has the desired distribution as its stationary distribution. The state of the chain after a number of steps is then used as a sample of the desired distribution.,How does MCMC sampling work,1352
183,10959,"Artificial intelligence has close connections with philosophy because both use concepts that have the same names and these include intelligence, action, consciousness, epistemology, and even free will.  These factors contributed to the emergence of the philosophy of artificial intelligence.",Is artificial intelligence possible philosophy,10959
184,8511,"Summary. The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It's easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.",What is K Nearest Neighbor machine learning,8511
185,8096,"Cross correlation and autocorrelation are very similar, but they involve different types of correlation: Cross correlation happens when two different sequences are correlated. Autocorrelation is the correlation between two of the same sequences. In other words, you correlate a signal with itself.",What is the difference between correlation and autocorrelation,8096
186,1448,Establish face validity.Conduct a pilot test.Enter the pilot test in a spreadsheet.Use principal component analysis (PCA)Check the internal consistency of questions loading onto the same factors.Revise the questionnaire based on information from your PCA and CA.,How do you establish reliability and validity of a questionnaire,1448
187,9114,"The regression slope intercept formula, b0 = y – b1 * x is really just an algebraic variation of the regression equation, y' = b0 + b1x where “b0” is the y-intercept and b1x is the slope. Once you've found the linear regression equation, all that's required is a little algebra to find the y-intercept (or the slope).",How do you find the Y intercept of a least squares regression line,9114
188,3267,"Neural network momentum is a simple technique that often improves both training speed and accuracy. Training a neural network is the process of finding values for the weights and biases so that for a given set of input values, the computed output values closely match the known, correct, target values.",What is momentum in neural network,3267
189,2424,"In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.",What is a good explanation of Latent Dirichlet Allocation,2424
190,4547,"The decision rule is: Reject H0 if Z < 1.645. The decision rule is: Reject H0 if Z < -1.960 or if Z > 1.960. The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in ""Other Resources.""",How do you find the decision rule,4547
191,3323,"When a data set has a negative value, the axis will be shifted upward by –MIN(R) where R is the data range containing the data. Thus if R ranges from -10 to 20, the range in the chart will range from 0 to 30.",How can the box plot chart have negative values,3323
192,6782,0:008:06Suggested clip · 106 secondsSPSS - Correspondence Analysis - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you do correspondence analysis in SPSS,6782
193,9581,Do you know how to choose the right machine learning algorithm among 7 different types?1-Categorize the problem.  2-Understand Your Data.  Analyze the Data.  Process the data.  Transform the data.  3-Find the available algorithms.  4-Implement machine learning algorithms.  5-Optimize hyperparameters.More items,How do I know which ML model to use,9581
194,8761,Recursive neural network models,Which model is best suited for recursive data,8761
195,6241,Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.,What is machine learning and its applications,6241
196,6598,"Communalities – This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables.",What is communality in factor analysis,6598
197,3257,"If X takes values in [a, b] and Y takes values in [c, d] then the pair (X, Y ) takes values in the product [a, b] × [c, d]. The joint probability density function (joint pdf) of X and Y is a function f(x, y) giving the probability density at (x, y).",How do you find the density of a joint function,3257
198,3264,A standard deviation is a measure of variability for a distribution of scores in a single sample or in a population of scores. A standard error is the standard deviation in a distribution of means of all possible samples of a given size from a particular population of individual scores.,What is the difference between standard deviation and standard error quizlet,3264
199,1201,Naive Bayes is a Supervised Machine Learning algorithm based on the Bayes Theorem that is used to solve classification problems by following a probabilistic approach. It is based on the idea that the predictor variables in a Machine Learning model are independent of each other.,What is naive Bayes in R,1201
200,4599,"Image processing is often viewed as arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. However, image processing is more accurately defined as a means of translation between the human visual system and digital imaging devices.",What is the importance of image processing,4599
201,9117,7 Top Linear Algebra Resources For Machine Learning BeginnersEssence Of Linear Algebra By 3Blue1Brown.Linear Algebra By Khan Academy.Basic Linear Algebra for Deep Learning By Niklas Donges.Computational Linear Algebra for Coders By fast.ai.Deep Learning Book By Ian Goodfellow and Yoshua Bengio and Aaron Courville.Linear Algebra for Machine Learning By AppliedAICourse.More items•,How do I learn linear algebra for machine learning,9117
202,6454,"Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval.",How can you tell the difference between continuous and discrete data,6454
203,10342,"Deep Reinforcement Learning: From Toys to Enteprise When paired with simulations, reinforcement learning is a powerful tool for training AI models that can help increase automation or optimize operational efficiency of sophisticated systems such as robotics, manufacturing, and supply chain logistics.",What is reinforcement learning good for,10342
204,3652,"Summing up, a more precise statement of the universality theorem is that neural networks with a single hidden layer can be used to approximate any continuous function to any desired precision.",Can neural networks approximate any function,3652
205,8745,"A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero.  A chi-square test tests a null hypothesis about the relationship between two variables.",What is the difference between T distribution and chi square distribution,8745
206,6776,"This is the idea behind the use of pooling in convolutional neural networks. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters, memory footprint and amount of computation in the network, and hence to also control overfitting.",Why is pooling used in convolutional neural network,6776
207,8873,Classic linear regression is one form of general linear model. But with a general linear model you can have any number of continuous or nominal independent variables and their interactions.,Can nominal variables be used in regression analysis,8873
208,9858,"Unlike humans, artificial neural networks are fed with massive amount of data to learn.  Also, real neurons do not stay on until the inputs change and the outputs may encode information using complex pulse arrangements.",Is there a difference between how humans and artificial neural networks learn,9858
209,616,"One of the most widely used predictive analytics models, the forecast model deals in metric value prediction, estimating numeric value for new data based on learnings from historical data. This model can be applied wherever historical numerical data is available.",Which model is used for prediction,616
210,5262,Offline evaluations test the effectiveness of recommender system algorithms on a certain dataset. Online evaluation attempts to evaluate recommender systems by a method called A/B testing where a part of users are served by recommender system A and the another part of users by recommender system B.,What is offline evaluation,5262
211,6748,This is why it is important to distinguish between the statistical significance of a result and the practical significance of that result.  Null hypothesis testing is a formal approach to deciding whether a statistical relationship in a sample reflects a real relationship in the population or is just due to chance.,What is the difference between statistical significance testing and null hypothesis testing,6748
212,253,"Bag of Words just creates a set of vectors containing the count of word occurrences in the document (reviews), while the TF-IDF model contains information on the more important words and the less important ones as well.",What is difference between Bag of Words and TF IDF,253
213,9559,"Multiple regression models forecast a variable using a linear combination of predictors, whereas autoregressive models use a combination of past values of the variable.  These concepts and techniques are used by technical analysts to forecast security prices.",What is difference between linear regression and autoregressive model in time series analysis,9559
214,5912,"When working with box plots, the IQR is computed by subtracting the first quartile from the third quartile. In a standard normal distribution (with mean 0 and standard deviation 1), the first and third quartiles are located at -0.67448 and +0.67448 respectively. Thus the interquartile range (IQR) is 1.34896.",How do you find the Iqr with the mean and standard deviation,5912
215,47,"To develop or improve your inductive reasoning, focus on the following skills: Paying attention to detail: No one can draw conclusions based on details without first noticing those details; paying attention is crucial to inductive reasoning.",How does one improve his or her inductive reasoning skills,47
216,4486,"Regression is the statistical model that you use to predict a continuous outcome on the basis of one or more continuous predictor variables. In contrast, ANOVA is the statistical model that you use to predict a continuous outcome on the basis of one or more categorical predictor variables.",When do you apply regression analysis and analysis of variance,4486
217,3093,It is one of several methods statisticians and researchers use to extract a sample from a larger population; other methods include stratified random sampling and probability sampling. The advantages of a simple random sample include its ease of use and its accurate representation of the larger population.,What are the advantages of simple random sampling,3093
218,9701,"Let's return to our example comparing the mean of a sample to a given value x using a t-test. Our null hypothesis is that the mean is equal to x. A one-tailed test will test either if the mean is significantly greater than x or if the mean is significantly less than x, but not both.",What is the difference between null hypothesis and alternative hypothesis one tailed tests and two tailed test,9701
219,7213,"Stochastic effects have been defined as those for which the probability increases with dose, without a threshold. Nonstochastic effects are those for which incidence and severity depends on dose, but for which there is a threshold dose. These definitions suggest that the two types of effects are not related.",What is the difference between stochastic and non stochastic,7213
220,5848,"A statistical project is the process of answering a research question using statistical techniques and presenting the work in a written report. The research question may arise from any field of scientific endeavor, such as athletics, advertising, aerodynamics, or nutrition.",What is a statistical project,5848
221,2607,"K-nearest neighbor (KNN) decision boundary K-nearest neighbor is an algorithm based on the local geometry of the distribution of the data on the feature hyperplane (and their relative distance measures). The decision boundary, therefore, comes up as nonlinear and non-smooth.",What is decision boundary in Knn,2607
222,3680,"Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning.",What is needed for deep learning,3680
223,10456,"There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.",How can we choose a good K for K means clustering,10456
224,4321,"Selectors are the names given to styles in internal and external style sheets. In this CSS Beginner Tutorial we will be concentrating on HTML selectors, which are simply the names of HTML tags and are used to change the style of a specific type of element.",What are selectors,4321
225,10897,"Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. Bivariate analysis can be helpful in testing simple hypotheses of association.",What is the purpose of bivariate analysis,10897
226,4130,k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.,Can Knn be used for clustering,4130
227,6658,Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.,Why we use logistic regression instead of linear regression,6658
228,8687,You can use the ArffViewer:(Tools -> ArffViewer or Ctrl+A). Then open your CSV file.Next go to File -> Save as and select Arff data files (should be selected by default.,How do I convert text files to arff format weka,8687
229,796,FFTs are great at analyzing vibration when there are a finite number of dominant frequency components; but power spectral densities (PSD) are used to characterize random vibration signals.,What is the difference between FFT and PSD,796
230,5461,"Explanation: Asynchronous update ensures that the next state is at most unit hamming distance from current state. 5. If pattern is to be stored, then what does stable state should have updated value of?",What is asynchronous update in Hopfield model,5461
231,3888,Object recognition is a computer vision technique for identifying objects in images or videos. Object recognition is a key output of deep learning and machine learning algorithms.  The goal is to teach a computer to do what comes naturally to humans: to gain a level of understanding of what an image contains.,What is object recognition in image processing,3888
232,5438,"The reason dividing by n-1 corrects the bias is because we are using the sample mean, instead of the population mean, to calculate the variance. Since the sample mean is based on the data, it will get drawn toward the center of mass for the data.",Why does dividing by n 1 instead of n remove the bias when calculating sample variance,5438
233,630,"Some of the main drawbacks of association rule algorithms in e-learning are: the used algorithms have too many parameters for somebody non expert in data mining and the obtained rules are far too many, most of them non-interesting and with low comprehensibility.",What is the limitations behind rule generation in association rule mining,630
234,2603,Backtracking is a technique based on algorithm to solve problem. It uses recursive calling to find the solution by building a solution step by step increasing values with time. It removes the solutions that doesn't give rise to the solution of the problem based on the constraints given to solve the problem.,What is backtracking algorithm in data structure,2603
235,3094,"Definition. Univariate analyses are used extensively in quality of life research. Univariate analysis is defined as analysis carried out on only one (“uni”) variable (“variate”) to summarize or describe the variable (Babbie, 2007; Trochim, 2006).",What do you mean by univariate analysis,3094
236,5015,"The SD line goes through the point of averages, and has slope equal to SDY/SDX if the correlation coefficient r is greater than or equal to zero. The SD line has slope −SDY/SDX if r is negative.  The line slopes up to the right, because r is positive (0.5 at first).",Does the regression line go through the point of averages,5015
237,4243,"Yes, the vectors from a word2vec model can be used as input in the learning of a new task, and in some (not all) cases, may yield better performance in the new model.",Does word2vec transfer learning,4243
238,8614,"Binomial counts successes in a fixed number of trials, while Negative binomial counts failures until a fixed number successes. The Bernoulli and Geometric distributions are the simplest cases of the Binomial and Negative Binomial distributions.",What is the difference between binomial and negative binomial distribution,8614
239,3354,"Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it.  Step 2: Learn advanced concepts, data structures, and algorithms.  Step 1+2: Practice.  Step 3: Lots of reading + writing.  Step 4: Contribute to open-source projects.  Step 5: Take a break.",How do I start learning algorithms,3354
240,2253,"The natural logarithm function is negative for values less than one and positive for values greater than one. So yes, it is possible that you end up with a negative value for log-likelihood (for discrete variables it will always be so).",Can the likelihood be negative,2253
241,8475,You will need to know the standard deviation of the population in order to calculate the sampling distribution. Add all of the observations together and then divide by the total number of observations in the sample.,How do you find the sampling distribution,8475
242,8560,"Cluster sampling is typically used in market research. It's used when a researcher can't get information about the population as a whole, but they can get information about the clusters. For example, a researcher may be interested in data about city taxes in Florida.",What is the purpose of cluster sampling,8560
243,3979,"Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron.",Why activation function is used in neural network,3979
244,4152,"Approximately Normal Distributions with Discrete Data. If a random variable is actually discrete, but is being approximated by a continuous distribution, a continuity correction is needed.",Can discrete random variables be normally distributed,4152
245,3491,"In mathematics, a nonnegative matrix, written. is a matrix in which all the elements are equal to or greater than zero, that is, A positive matrix is a matrix in which all the elements are strictly greater than zero.",What is non negative matrix,3491
246,7810,Explanation: The objective of perceptron learning is to adjust weight along with class identification.,What is the objective of Perceptron learning *,7810
247,794,"It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance.  The rectified linear activation function overcomes the vanishing gradient problem, allowing models to learn faster and perform better.",Why is rectified linear unit a good activation function,794
248,8833,The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The T test is also used if you have a small sample size (less than 30).,What is Z statistics and t statistics,8833
249,7828,An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. The ROC curve is a graph with: The x-axis showing 1 – specificity (= false positive fraction = FP/(FP+TN)) The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN)),What does a ROC curve tell you,7828
250,9048,4:551:11:29Suggested clip · 112 secondsRodrigo Agundez: Building a live face recognition system | Pydata YouTubeStart of suggested clipEnd of suggested clip,How do you create a face recognition system,9048
251,6996,"One such step is eliminating duplicate data as discussed above. Another step is resolving any conflicting data. Sometimes, datasets will have information that conflicts with each other, so data normalization is meant to address this conflicting issue and solve it before continuing. A third step is formatting the data.",When and why do we need data normalization,6996
252,8236,"Markov models are useful to model environments and problems involving sequential, stochastic decisions over time. Representing such environments with decision trees would be confusing or intractable, if at all possible, and would require major simplifying assumptions [2].",Why Markov model is useful,8236
253,403,"To understand potential interaction effects, compare the lines from the interaction plot: If the lines are parallel, there is no interaction. If the lines are not parallel, there is an interaction.",How do you know if there is an interaction effect,403
254,365,"A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.  See Get ONNX models for Windows ML for more information.",What is ML model,365
255,10927,"With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population.  For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling.",What is the difference between simple random sampling and cluster sampling,10927
256,3591,"The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression.",Are neural networks good for regression,3591
257,3540,"Random forest will reduce variance part of error rather than bias part, so on a given training data set decision tree may be more accurate than a random forest. But on an unexpected validation data set, Random forest always wins in terms of accuracy.",How is random forest better than decision tree,3540
258,3247,Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter.  An example of a decision tree can be explained using above binary tree.,What is decision trees in machine learning,3247
259,8274,"Decision tree is unstable because training a tree with a slightly different sub-sample causes the structure of the tree to change drastically. It overfits by learning from noise data as well and optimises for that particular sample, which causes its variable importance order to change significantly.",Why are decision trees unstable,8274
260,4979,Types of machine learning AlgorithmsSupervised learning.Unsupervised Learning.Semi-supervised Learning.Reinforcement Learning.,What are the different types of learning that algorithms use,4979
261,9972,Here are five ways to identify segments.Cross-Tab. Cross-tabbing is the process of examining more than one variable in the same table or chart (“crossing” them).  Cluster Analysis.  Factor Analysis.  Latent Class Analysis (LCA)  Multidimensional Scaling (MDS),How do you find clusters in data,9972
262,1098,"The k-modes algorithm tries to minimize the sum of within-cluster Hamming distance from the mode of that cluster, summed over all clusters.  The procedure is similar to k-means: a number of clusters (k) is chosen, and k cluster-mode vectors are chosen at random (or according to accepted heuristics).",How does K modes work,1098
263,10491,• h is the Vapnik Chervonenkis (VC) dimension and is a measure of the capacity or complexity of the machine.,What is VC dimension in SVM,10491
264,10745,Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem.  Then it assigns random weights to those networks and employs genetic algorithm to evolve the weights so that they can characterize to some extent the fitness of the neural networks in constituting an ensemble.,What is ensemble neural network,10745
265,10523,"So, if you are constrained either by the size of the data or the number of trials you want to try, you may have to go with random forests. There is one fundamental difference in performance between the two that may force you to choose Random Forests over Gradient Boosted Machines (GBMs).",When would one use Random Forests over Gradient Boosted Machines GBMs,10523
266,8118,"In statistics, the method of moments is a method of estimation of population parameters. It starts by expressing the population moments (i.e., the expected values of powers of the random variable under consideration) as functions of the parameters of interest.  The solutions are estimates of those parameters.",What is the method of moments estimator,8118
267,1368,Using the Interquartile Rule to Find Outliers Multiply the interquartile range (IQR) by 1.5 (a constant used to discern outliers). Add 1.5 x (IQR) to the third quartile. Any number greater than this is a suspected outlier. Subtract 1.5 x (IQR) from the first quartile.,How do you use interquartile range to find outliers,1368
268,5752,"The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate.  GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM.",Are GRU Gated Recurrent Unit a special case of LSTM,5752
269,3138,"The low-pass filter has a gain response with a frequency range from zero frequency (DC) to ωC. Any input that has a frequency below the cutoff frequency ωC gets a pass, and anything above it gets attenuated or rejected. The gain approaches zero as frequency increases to infinity.",What is the frequency response of low pass filter,3138
270,4865,A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.,What is Markov chain in probability,4865
271,2125,"Another strategy OTs typically recommend is something called “backward chaining."" Backward chaining is working backward from the goal. For example, the goal is put on a T-shirt.  Pull shirt over head. Push right arm up through right sleeve.",What is an example of backward chaining,2125
272,4700,"Differential calculus is usually taught first. I think most students find it more intuitive because they deal with rates of change in real life. Integral calculus is more abstract, and indefinite integrals are much easier to evaluate if you understand differentiation.",Which comes first differential or integral calculus,4700
273,1315,Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset.  Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features.,What is Dimension reduction in machine learning,1315
274,1581,1. A Multi-Agent System (MAS) is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each software agent. Learn more in: Using Multi-Agent Systems to Support e-Health Services. A system composed of multiple interacting intelligent agents,What are multi agent systems 1,1581
275,7160,"Theorem 1.2 Suppose that ψ is a simple random point process that has both stationary and independent increments. Then in fact, ψ is a Poisson process. Thus the Poisson process is the only simple point process with stationary and independent increments.",Is Poisson process stationary,7160
276,3561,"Some of the most popular methods for outlier detection are:Z-Score or Extreme Value Analysis (parametric)Probabilistic and Statistical Modeling (parametric)Linear Regression Models (PCA, LMS)Proximity Based Models (non-parametric)Information Theory Models.More items",What methods do you use to identify outliers within a data set,3561
277,3644,"Reduce Variance of an Estimate If we want to reduce the amount of variance in a prediction, we must add bias. Consider the case of a simple statistical estimate of a population parameter, such as estimating the mean from a small random sample of data. A single estimate of the mean will have high variance and low bias.",How do you reduce the variance of data,3644
278,2628,The lognormal distribution is a probability distribution whose logarithm has a normal distribution. The mean m and variance v of a lognormal random variable are functions of the lognormal distribution parameters µ and σ: m = exp ( μ + σ 2 / 2 ) v = exp ( 2 μ + σ 2 ) ( exp ( σ 2 ) − 1 ),What is the mean and variance of lognormal distribution,2628
279,5279,"2. Exponential Moving Average (EMA) The other type of moving average is the exponential moving average (EMA), which gives more weight to the most recent price points to make it more responsive to recent data points.",Which moving average is more responsive,5279
280,3303,A batch size of 32 means that 32 samples from the training dataset will be used to estimate the error gradient before the model weights are updated.,Why is batch size 32,3303
281,4099,Epsilon is used when we are selecting specific actions base on the Q values we already have. As an example if we select pure greedy method ( epsilon = 0 ) then we are always selecting the highest q value among the all the q values for a specific state.,What is Epsilon in Q learning,4099
282,433,Variance,Which gives the measure of randomness of the random variable,433
283,7053,"The technological singularity—also, simply, the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.",What is it called when AI becomes self aware,7053
284,2936,"Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning.  LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.",What is Lstm in neural network,2936
285,858,Feature Selection.  The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.,What is the difference between feature selection and feature extraction,858
286,1717,"Digital image processing, as a computer-based technology, carries out automatic processing, manipulation and interpretation of such visual information, and it plays an increasingly important role in many aspects of our daily life, as well as in a wide variety of disciplines and fields in science and technology, with",What are the application of image processing,1717
287,2618,"1. Agglomerative approach: This method is also called a bottom-up approach shown in Figure 6.7. In this method, each node represents a single cluster at the beginning; eventually, nodes start merging based on their similarities and all nodes belong to the same cluster.",Which of clustering algorithms is called bottom up approach,2618
288,9874,"Introduction. The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population. However, as we are often presented with data from a sample only, we can estimate the population standard deviation from a sample standard deviation.",Do you use sample or population standard deviation,9874
289,3942,"The mean of a discrete random variable X is a weighted average of the possible values that the random variable can take. Unlike the sample mean of a group of observations, which gives each observation equal weight, the mean of a random variable weights each outcome xi according to its probability, pi.",What is the mean of a discrete random variable,3942
290,6459,"There are often only a handful of possible classes or results. For a given classification, one tries to measure the probability of getting different evidence or patterns.  Using Bayes rule, we use this to get what is desired, the conditional probability of the classification given the evidence.",Why do you think you need conditional probability,6459
291,5497,"The essential difference between these two is that Logistic regression is used when the dependent variable is binary in nature. In contrast, Linear regression is used when the dependent variable is continuous and nature of the regression line is linear.",What are the two main differences between logistic regression and linear regression,5497
292,4963,"The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test).  a lower-tailed test is specified by: p-value = P(TS ts | H 0 is true) = cdf(ts)",How is P value calculated,4963
293,5295,"Nonresponse bias occurs when some respondents included in the sample do not respond. The key difference here is that the error comes from an absence of respondents instead of the collection of erroneous data.  Most often, this form of bias is created by refusals to participate or the inability to reach some respondents.",How does non response cause bias,5295
294,3967,"The survival function is a function that gives the probability that a patient, device, or other object of interest will survive beyond any specified time. The survival function is also known as the survivor function or reliability function.",What does survival function mean,3967
295,1975,The test statistic used in ANOVA is Student's t. One characteristic of the F distribution is that F cannot be negative. One characteristic of the F distribution is that the computed F can only range between -1 and +1.,What is a characteristic of the F distribution that is used in Anova,1975
296,8881,"A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000.",What is a good representative sample size,8881
297,4336,Use systematic sampling when there's low risk of data manipulation. Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation.,When would you use systematic sampling,4336
298,2657,When Longitudinal data looks like a time series is when we measure the same thing over time. The big difference is that in a time series we can measure the overall change in the measurement over time (or by group) while in a longitudinal analysis you actually have the measurement of change at the individual level.,What is the difference between time series and longitudinal data,2657
299,8798,"Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.",What does transfer learning mean,8798
300,9459,"Compare r to the appropriate critical value in the table. If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points.",How do you know if a correlation coefficient is statistically significant,9459
301,5003,"A factorial ANOVA compares means across two or more independent variables. Again, a one-way ANOVA has one independent variable that splits the sample into two or more groups, whereas the factorial ANOVA has two or more independent variables that split the sample in four or more groups.",What is the difference between one way Anova and factorial Anova,5003
302,4178,If you want to process the gradients before applying them you can instead use the optimizer in three steps:Compute the gradients with compute_gradients().Process the gradients as you wish.Apply the processed gradients with apply_gradients().,How does one do gradient clipping in TensorFlow,4178
303,7689,"A disadvantage is when researchers can't classify every member of the population into a subgroup. Stratified random sampling is different from simple random sampling, which involves the random selection of data from the entire population so that each possible sample is equally likely to occur.",What are the drawbacks disadvantage of stratified sampling,7689
304,6985,"Once you have generated a prediction model (also called training a model), you can put it to use making predictions.  The scoring process examines a dataset and predicts results for each record based on similarities to records analyzed during model training.",What is the predictive score model,6985
305,4962,"Squaring the residuals, averaging the squares, and taking the square root gives us the r.m.s error. You then use the r.m.s. error as a measure of the spread of the y values about the predicted y value.",How do you find the root mean square error,4962
306,2662,A support vector machine is a machine learning model that is able to generalise between two different classes if the set of labelled data is provided in the training set to the algorithm. The main function of the SVM is to check for that hyperplane that is able to distinguish between the two classes.,How do support vector machines work,2662
307,1738,"Hidden Markov model (HMM) has been successfully used for sequential data modeling problems.  In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation.",Is a hidden Markov model a neural network,1738
308,4029,"Softmax is a non-linear activation function, and is arguably the simplest of the set. In this expression, zi is the current value. The denominator in the expression is the sum across every value passed to a node in the layer.",Is Softmax a linear function,4029
309,3192,A Classification and Regression Tree(CART) is a predictive algorithm used in machine learning. It explains how a target variable's values can be predicted based on other values. It is a decision tree where each fork is a split in a predictor variable and each node at the end has a prediction for the target variable.,What is a classification and regression tree CART,3192
310,1942,"The sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase.",How do you interpret regression results,1942
311,479,"The square of the correlation coefficient, r², is a useful value in linear regression. This value represents the fraction of the variation in one variable that may be explained by the other variable.  The correlation coefficient also relates directly to the regression line Y = a + bX for any two variables, where .",How is the regression line related to the correlation coefficient,479
312,3802,A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population.  Comprehensiveness refers to the degree to which a sampling frame covers the entire target population.,What do you mean by sampling frame,3802
313,7795,"Abstract: The generalized likelihood ratio test (GLRT), which is commonly used in composite hypothesis testing problems, is investigated. Conditions for asymptotic optimality of the GLRT in the Neyman-Pearson sense are studied and discussed.",What is GLRT,7795
314,8610,"Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages.",What is multimodal machine learning,8610
315,2580,"You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper.  It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.",Do you need to know Linear Algebra for machine learning,2580
316,2947,"The margin of error increases as the level of confidence increases because the larger the expected proportion of intervals that will contain the​ parameter, the larger the margin of error.  The larger the level of confidence​ is, the larger number of intervals that will contain the parameter.",What increases the margin of error,2947
317,1772,"It is often used as a gauge of economic inequality, measuring income distribution or, less commonly, wealth distribution among a population. The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality.",What does a lower Gini coefficient mean,1772
318,6692,"""The difference between discrete choice models and conjoint models is that discrete choice models present experimental replications of the market with the focus on making accurate predictions regarding the market, while conjoint models do not, using product profiles to estimate underlying utilities (or partworths)",What is the difference between a discrete choice and a conjoint analysis,6692
319,4177,"The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR).",How do you read a ROC curve,4177
320,5260,"Variational Bayesian methods are primarily used for two purposes: To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables.",Why do variational inferences occur,5260
321,7639,A machine-learning algorithm that involves a Gaussian process uses lazy learning and a measure of the similarity between points (the kernel function) to predict the value for an unseen point from training data.,What is Gaussian process in machine learning,7639
322,860,"It depends. If the message you want to carry is about the spread and variability of the data, then standard deviation is the metric to use. If you are interested in the precision of the means or in comparing and testing differences between means then standard error is your metric.",Can you use standard error instead of standard deviation,860
323,4117,"Active learning engages students in learning, using activities such as reading, writing, discussion, or problem solving, which promote analysis, synthesis, and evaluation of class content. Active in-class learning also provides students with informal opportunities for feedback on how well they understood the material.",What are active learning strategies,4117
324,3125,"Joint probability is calculated by multiplying the probability of event A, expressed as P(A), by the probability of event B, expressed as P(B). For example, suppose a statistician wishes to know the probability that the number five will occur twice when two dice are rolled at the same time.",How do you calculate joint probability,3125
325,8068,"In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation.  The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value.",Why is vanishing gradient a problem,8068
326,6109,"Box plots are useful as they show outliers within a data set. An outlier is an observation that is numerically distant from the rest of the data. When reviewing a box plot, an outlier is defined as a data point that is located outside the whiskers of the box plot.",Can a Boxplot be used to detect outliers,6109
327,4180,"Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1.",What are weights in machine learning,4180
328,10593,Adam can be looked at as a combination of RMSprop and Stochastic Gradient Descent with momentum. It uses the squared gradients to scale the learning rate like RMSprop and it takes advantage of momentum by using moving average of the gradient instead of gradient itself like SGD with momentum.,How does Adam Optimizer work,10593
329,10945,"To find the mean absolute deviation of the data, start by finding the mean of the data set. Find the sum of the data values, and divide the sum by the number of data values. Find the absolute value of the difference between each data value and the mean: |data value – mean|.",How do you get the mean absolute deviation,10945
330,3427,Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.,What is the exact meaning of artificial intelligence,3427
331,2389,"In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a",What is Kalman filter used for,2389
332,9828,"A (non-mathematical) definition I like by Miller (2017)3 is: Interpretability is the degree to which a human can understand the cause of a decision.  The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made.",What is model interpretability,9828
333,9531,"In a histogram, the total range of data set (i.e from minimum value to maximum value) is divided into 8 to 15 equal parts. These equal parts are known as bins or class intervals. Each and every observation (or value) in the data set is placed in the appropriate bin.",What is binning in histogram,9531
334,10256,Sampling is used any time data is to be gathered. Data cannot be collected until the sample size (how much) and sample frequency (how often) have been determined. Sampling should be periodically reviewed.,Under what circumstances sampling is used,10256
335,7454,"TensorFlow applications can be run on most any target that's convenient: a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs. If you use Google's own cloud, you can run TensorFlow on Google's custom TensorFlow Processing Unit (TPU) silicon for further acceleration.",Where can I use TensorFlow,7454
336,10408,"A one-tailed test is also known as a directional hypothesis or directional test. A two-tailed test, on the other hand, is designed to examine both sides of a specified data range to test whether a sample is greater than or less than the range of values.",What is the differences between marginal distribution and the two tailed test,10408
337,1335,Note the difference between parameters and arguments: Function parameters are the names listed in the function's definition. Function arguments are the real values passed to the function. Parameters are initialized to the values of the arguments supplied.,What is the difference between an argument and a parameter,1335
338,1316,"The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. In word2vec, a distributed representation of a word is used.",What is the output of Word2Vec,1316
339,3166,Creating A Target VariableFrom the menu: Click View > User Variables. The Variables dialog box appears. Click Add Target.From the Target pane: Right-click a linked field and select Edit Lookup Criteria. The Edit Lookup Criteria for the selected field appears. Click Edit Lookup Formula. The Edit Formula for the selected field appears.,How do you create a target variable,3166
340,6906,"This occurs when the line-of-best-fit for describing the relationship between x and y is a straight line. The linear relationship between two variables is positive when both increase together; in other words, as values of x get larger values of y get larger. This is also known as a direct relationship.",How do you tell if there is a linear relationship between two variables,6906
341,1060,Root Mean Squared Error or RMSE RMSE is the standard deviation of the errors which occur when a prediction is made on a dataset. This is the same as MSE (Mean Squared Error) but the root of the value is considered while determining the accuracy of the model. from sklearn.,What is root mean square error in machine learning,1060
342,2673,"In statistics and regression analysis, moderation occurs when the relationship between two variables depends on a third variable. The third variable is referred to as the moderator variable or simply the moderator.",If you control for a variable and examine the relationship between two others is this moderation,2673
343,8653,"Continuous probability distribution: A probability distribution in which the random variable X can take on any value (is continuous). Because there are infinite values that X could assume, the probability of X taking on any one specific value is zero.  The normal distribution is one example of a continuous distribution.",What is continuous probability distribution,8653
344,3498,"In the real world, knowledge plays a vital role in intelligence as well as creating artificial intelligence. It demonstrates the intelligent behavior in AI agents or systems. It is possible for an agent or system to act accurately on some input only when it has the knowledge or experience about the input.",What is the role of knowledge in AI,3498
345,4171,"The lognormal distribution is a distribution skewed to the right. The pdf starts at zero, increases to its mode, and decreases thereafter. The degree of skewness increases as increases, for a given . For the same , the pdf's skewness increases as increases.",What properties do a log normal distribution have,4171
346,9244,"A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.",What is an example of a stratified sample,9244
347,9993,"Under the hood, these RDDs are stored in partitions on different cluster nodes. Partition basically is a logical chunk of a large distributed data set. It provides the possibility to distribute the work across the cluster, divide the task into smaller parts, and reduce memory requirements for each node.",How is RDD partitioned,9993
348,10263,Multinomial logistic regression is used when you have a categorical dependent variable with two or more unordered levels (i.e. two or more discrete outcomes).  One level of the dependent variable is chosen as the reference category. This is typically the most common or the most frequent category.,What is a multinomial variable,10263
349,5463,Random errors in experimental measurements are caused by unknown and unpredictable changes in the experiment. These changes may occur in the measuring instruments or in the environmental conditions.,What is random error,5463
350,4402,"If the correlation between education and unobserved ability is positive, omitted variables bias will occur in an upward direction. Conversely, if the correlation between an explanatory variable and an unobserved relevant variable is negative, omitted variables bias will occur in a downward direction.",How do you determine the direction of omitted variable bias,4402
351,3430,"Len Gould. Answered November 6, 2016 · Author has 6.4K answers and 3M answer views. Outgroups are simply the people who are not members of your ingroup. Obvious examples of bases for forming ingroups are according to their race, culture, gender, age or religion.",What is ingroup and outgroup examples,3430
352,3608,"The difference between true random number generators(TRNGs) and pseudo-random number generators(PRNGs) is that TRNGs use an unpredictable physical means to generate numbers (like atmospheric noise), and PRNGs use mathematical algorithms (completely computer-generated).",What is the difference between random number and pseudo random number,3608
353,5649,"Nonparametric tests are also called distribution-free tests because they don't assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don't meet the assumptions of the parametric test, especially the assumption about normally distributed data.",In the field of statistics when are nonparametric tests preferred over parametric tests,5649
354,3339,"Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics such as income or educational attainment.",What is a stratified random sample,3339
355,4140,Systematic random samplingCalculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)Select a random start between 1 and sampling interval.Repeatedly add sampling interval to select subsequent households.,How do you do systematic sampling,4140
356,3060,"Bivariate analysis means the analysis of bivariate data. It is one of the simplest forms of statistical analysis, used to find out if there is a relationship between two sets of values. It usually involves the variables X and Y. Univariate analysis is the analysis of one (“uni”) variable.",When will we use bivariate analysis,3060
357,9282,"In deep multilayer Perceptron networks, exploding gradients can result in an unstable network that at best cannot learn from the training data and at worst results in NaN weight values that can no longer be updated. … exploding gradients can make learning unstable.",Do ReLU networks suffer from the exploding gradient problem,9282
358,7073,"Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.",Why is deep learning needed,7073
359,4060,Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.,What is the difference between working in analytics and data science,4060
360,4921,"Definition. Stimulus generalization is the tendency of a new stimulus to evoke responses or behaviors similar to those elicited by another stimulus. For example, Ivan Pavlov conditioned dogs to salivate using the sound of a bell and food powder.",What is an example of stimulus generalization,4921
361,17,4.3 The method: evolutionary computation. EC is a computational intelligence technique inspired from natural evolution. An EC algorithm starts with creating a population consisting of individuals that represent solutions to the problem. The first population could be created randomly or fed into the algorithm.,What is evolutionary computation in AI,17
362,165,"In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system's algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is an established way of testing the capabilities of that system.",How does unsupervised machine learning work,165
363,10380,"ReLu bounded negative outputs to 0 & above. This works well in hidden layers than the final output layer.  It is not typical, since in this case, the ouput value is not bounded in a range.",Why ReLU is not used in output layer,10380
364,3828,four outcomes,How many outcomes are in the sample space,3828
365,6926,"Bias can damage research, if the researcher chooses to allow his bias to distort the measurements and observations or their interpretation. When faculty are biased about individual students in their courses, they may grade some students more or less favorably than others, which is not fair to any of the students.",What is the problem with bias,6926
366,1378,"Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it.  Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science. Machine learning performs tasks where human interaction doesn't matter.",Is Big Data Machine Learning,1378
367,9712,"Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it.  Step 2: Learn advanced concepts, data structures, and algorithms.  Step 1+2: Practice.  Step 3: Lots of reading + writing.  Step 4: Contribute to open-source projects.  Step 5: Take a break.",What is the best way to learn algorithms,9712
368,313,"The key assumption in ordinal regression is that the effects of any explanatory variables are consistent or proportional across the different thresholds, hence this is usually termed the assumption of proportional odds (SPSS calls this the assumption of parallel lines but it's the same thing).",What are the assumptions of ordinal logistic regression,313
369,1994,"– Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network. – Test set: A set of examples used only to assess the performance of a fully-specified classifier.",What is the difference between test set and validation set,1994
370,9366,"When all the points on a scatterplot lie on a straight line, you have what is called a perfect correlation between the two variables (see below). A scatterplot in which the points do not have a linear trend (either positive or negative) is called a zero correlation or a near-zero correlation (see below).",Is it possible for a scatter plot to have a positive or negative association that is not linear,9366
371,1615,"A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.",What are generative models used for,1615
372,4393,A random variable is a numerical description of the outcome of a statistical experiment. A random variable that may assume only a finite number or an infinite sequence of values is said to be discrete; one that may assume any value in some interval on the real number line is said to be continuous.,What is a random variable in statistics,4393
373,10171,"Intuitively, two random variables X and Y are independent if knowing the value of one of them does not change the probabilities for the other one. In other words, if X and Y are independent, we can write P(Y=y|X=x)=P(Y=y), for all x,y.",Does random variables imply independence,10171
374,4033,Explanation: The two types of Fourier series are- Trigonometric and exponential.,What are the two types of Fourier series,4033
375,257,The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line). You can look at support vector machines and a few examples of its working here.,What are the two classification methods that SVM support vector machine can handle,257
376,1735,"The main difference between the two, is that a Perceptron takes that binary response (like a classification result) and computes an error used to update the weights, whereas an Adaline uses a continous response value to update the weights (so before the binarized output is produced).",What is the difference between a Perceptron Adaline and neural network model,1735
377,5091,Hierarchical regression is a way to show if variables of your interest explain a statistically significant amount of variance in your Dependent Variable (DV) after accounting for all other variables. This is a framework for model comparison rather than a statistical method.,What is hierarchical regression used for,5091
378,3559,"The Non-Linear Decision Boundary SVM works well when the data points are linearly separable. If the decision boundary is non-liner then SVM may struggle to classify. Observe the below examples, the classes are not linearly separable. SVM has no direct theory to set the non-liner decision boundary models.",What is non linear decision boundary,3559
379,1071,"In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA).",What is ARMA model used for,1071
380,4468,"The parameters of LDA model have the prior distribution, and are estimated by Bayesian method. LDA model has attracted many scholars' attention since its start, but its mathematical theory is too complex to understand quickly.",Is LDA a Bayesian,4468
381,9141,"Example 1: Fair Dice Roll The number of desired outcomes is 3 (rolling a 2, 4, or 6), and there are 6 outcomes in total. The a priori probability for this example is calculated as follows: A priori probability = 3 / 6 = 50%. Therefore, the a priori probability of rolling a 2, 4, or 6 is 50%.",How do you calculate a priori probability,9499
382,839,The SVM in particular defines the criterion to be looking for a decision surface that is maximally far away from any data point. This distance from the decision surface to the closest data point determines the margin of the classifier.  Figure 15.1 shows the margin and support vectors for a sample problem.,What is a margin in SVM,839
383,10785,Interpret the key results for Fit Mixed Effects ModelStep 1: Determine whether the random terms significantly affect the response.Step 2: Determine whether the fixed effect terms significantly affect the response.Step 3: Determine how well the model fits your data.Step 4: Evaluate how each level of a fixed effect term affects the response.More items,How do you read mixed model results,10785
384,8371,"EXAMPLES OF DATA MINING APPLICATIONS Marketing. Data mining is used to explore increasingly large databases and to improve market segmentation.  It is commonly applied to credit ratings and to intelligent anti-fraud systems to analyse transactions, card transactions, purchasing patterns and customer financial data.",What is data mining and example,8371
385,6050,"E(Y | Xi) = f (Xi) is known as conditional expectation function(CEF) or population regression function (PRF) or population regression (PR) for short. In simple terms, it tells how the mean or average of response of Y varies with X.",What is the conditional expectation function or the population regression function,6050
386,10947,NAT (Network Address Translation) is a feature of the Firewall Software Blade and replaces IPv4 and IPv6 addresses to add more security. You can enable NAT for all SmartDashboard objects to help manage network traffic. NAT protects the identity of a network and does not show internal IP addresses to the Internet.,What is a NAT policy,10947
387,3337,Epsilon is used when we are selecting specific actions base on the Q values we already have.  In conclusion learning rate is associated with how big you take a leap and epsilon is associated with how random you take an action.,What is Epsilon in reinforcement learning,3337
388,518,"A psychometric and capability test aims to provide measurable, objective data that can give you a better versatile view of a candidate's skills and suitability for a position. Assessments offer scientific, valid reliable and objectivity to the process of recruiting.",What is psychometric and skills testing,518
389,4143,"Statistical classification helps in determining the set to which a particular observation belongs. Multiple methods can be used for the classification process, namely, Frequentest procedure and Bayesian procedure among others. It helps in quicker arranging and collection of data,as well as more efficient work rate.",What is statistical classification What is the importance of such a classification,4143
390,6833,A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. The difference between a population and a sampling frame is that the population is general and the frame is specific.,Is sampling frame the same as population,6833
391,914,"Because a researcher rarely has direct access to the entire population of interest in social science research, a researcher must rely upon a sampling frame to represent all of the elements of the population of interest. Generally, sampling frames can be divided into two types, list and nonlist.",What is the importance of a sample frame,914
392,5419,"The random (or precision) error for this data point is defined as the reading minus the average of readings, or -1.20 - (-1.42) = 0.22oC. Thus, the maximum absolute value of random error is 0.22oC. You can verify that the magnitude of the random error for any of the other data points is less than this.",How do you find the maximum random error,5419
393,2891,"Gladwell's purpose for writing The Outliers was to inform reader's on how successful people achieve success through the help of others, practice, and opportunity. He also wanted to get rid of our society's crude perspective on how outliers become successful.",What is Gladwell's purpose in outliers,2891
394,1309,"Well, if you break down the words, forward implies moving ahead and propagation is a term for saying spreading of anything. forward propagation means we are moving in only one direction, from input to the output, in a neural network.",What is forward propagation in machine learning,1309
395,8980,"Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal. A normal distribution is perfectly symmetrical around its center. That is, the right side of the center is a mirror image of the left side. There is also only one mode, or peak, in a normal distribution.",What are the characteristics of a normal distribution,8980
396,4012,Machine learning has a limited scope. AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained. AI system is concerned about maximizing the chances of success.,What is the distinction between artificial intelligence AI and machine learning from your perspective what are some of the legitimate concerns about the future of AI,4012
397,10562,The reason why Convolutional Neural Networks (CNNs) do so much better than classic neural networks on images and videos is that the convolutional layers take advantage of inherent properties of images. Simple feedforward neural networks don't see any order in their inputs.,Why convolutional neural networks are better suited for image recognition than fully connected networks,10562
398,8822,"To convert this distance metric into the similarity metric, we can divide the distances of objects with the max distance, and then subtract it by 1 to score the similarity between 0 and 1. We will look at the example after discussing the cosine metric.",How do you measure similarity,8822
399,3678,The Euclidean distance corresponds to the L2-norm of a difference between vectors. The cosine similarity is proportional to the dot product of two vectors and inversely proportional to the product of their magnitudes.,Why cosine similarity is better than Euclidean distance,3678
400,380,"It will be easier to learn and use. If you are in the industry where you need to deploy models in production, Tensorflow is your best choice. You can use Keras/Pytorch for prototyping if you want. But you don't need to switch as Tensorflow is here to stay.",Should I use PyTorch or TensorFlow,380
401,10459,"In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a ""false positive"" finding or conclusion; example: ""an innocent person is convicted""), while a type II error is the non-rejection of a false null hypothesis (also known as a ""false negative"" finding or conclusion",What is the difference between Type 1 and Type 2 error in statistics,10459
402,9429,"Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population. For example, Lucas can give a survey to every fourth customer that comes in to the movie theater.",What is systematic random sampling with example,9429
403,9753,"Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid.",What does a neuron compute in neural network,9753
404,5311,"If you are studying one group, use a paired t-test to compare the group mean over time or after an intervention, or use a one-sample t-test to compare the group mean to a standard value. If you are studying two groups, use a two-sample t-test. If you want to know only whether a difference exists, use a two-tailed test.",How do you know what t test to use,5311
405,7024,"Deep learning techniques do not perform well when dealing with data with complex hierarchical structures. Deep learning identifies correlations between sets of features that are themselves “flat” or non-hierarchical, as in a simple, unstructured list, but much human and linguistic knowledge is more structured.",What deep learning Cannot do,7024
406,2737,"Below are the steps to implement the handwritten digit recognition project:Import the libraries and load the dataset. First, we are going to import all the modules that we are going to need for training our model.  Preprocess the data.  Create the model.  Train the model.  Evaluate the model.  Create GUI to predict digits.",How do you make a digit recognizer,2737
407,2413,"The first benefit of time series analysis is that it can help to clean data. This makes it possible to find the true “signal” in a data set, by filtering out the noise. This can mean removing outliers, or applying various averages so as to gain an overall perspective of the meaning of the data.",What are the advantages of time series analysis,2413
408,8411,"Our Big Data Hadoop certification training course lets you master the concepts of the Hadoop framework, Big Data tools, and methodologies to prepare you for success in your role as a Big Data Developer. Learn how various components of the Hadoop ecosystem fit into the Big Data processing lifecycle.",What is big data Course,8411
409,7968,"Very expensive voltmeters are often made to measure “true RMS”, because that is what is desired. Low-cost voltmeters approximate the RMS value. To approximate the RMS value for a sine wave, one could simply find the peak value of the sine wave and multiply it by .",Do voltmeters measure RMS or peak,7968
410,7185,"Sometimes we are given a chart showing frequencies of certain groups instead of the actual values.  If we multiply each midpoint by its frequency, and then divide by the total number of values in the frequency distribution, we have an estimate of the mean.",What does frequency distribution mean,7185
411,9234,The discriminator in a GAN is simply a classifier. It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it's classifying. Figure 1: Backpropagation in discriminator training.,What is discriminator in Gan,9234
412,8168,We will use the RAND() function to generate a random value between 0 and 1 on our Y-axis and then get the inverse of it with the NORM. INV function which will result in our random normal value on the X-axis. Mean – This is the mean of the normal distribution.,How do you generate a random number from a normal distribution,8168
413,2600,The collaborative filtering algorithm uses “User Behavior” for recommending items. This is one of the most commonly used algorithms in the industry as it is not dependent on any additional information.,Which algorithms are used in recommendation system,2600
414,9892,"Qualitative Variables - Variables that are not measurement variables. Their values do not result from measuring or counting. Examples: hair color, religion, political party, profession. Designator - Values that are used to identify individuals in a table.",What is qualitative variable,9892
415,2617,The skip-gram model. Both the input vector x and the output y are one-hot encoded word representations. The hidden layer is the word embedding of size N.,Which layer of the skip gram model has an actual word embedding representation,2617
416,68,"Values range from 0 to 1, where 0 is perfect disagreement and 1 is perfect agreement. Krippendorff suggests: “[I]t is customary to require α ≥ . 800. Where tentative conclusions are still acceptable, α ≥ .",What is a good krippendorff's Alpha,68
417,8467,"load_model functionv2. 0. Load a model from a shortcut link, package or data path. If called with a shortcut link or package name, spaCy will assume the model is a Python package and import and call its load() method.",Which function is used to load a model in spaCy,8467
418,9287,"All Answers (8) A matrix is a two dimensional array of numbers (or values from some field or ring). A 2-rank tensor is a linear map from two vector spaces, over some field such as the real numbers, to that field.",What is the difference between a matrix and a tensor,9287
419,6277,"The higher the threshold, or closer to (0, 0), the higher the specificity and the lower the sensitivity. The lower the threshold, or closer to (1,1), the higher the sensitivity and lower the specificity. So which threshold value one should pick?",How do you choose the threshold in logistic regression,6277
420,6290,"Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss.  By default, multi_class is set to 'ovr'.",Can we use logistic regression for multi class classification,6290
421,18,"Cluster Analysis and Factor Analysis. Latent Class Analysis is similar to cluster analysis. Observed data is analyzed, connections are found, and the data is grouped into clusters.  Another difference is that LCA includes discrete latent categorical variables that have a multinomial distribution.",What is the difference between cluster analysis and latent class analysis,18
422,3203,"AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.",What is the difference between machine learning,3203
423,7525,Log-loss is an appropriate performance measure when you're model output is the probability of a binary outcome. The log-loss measure considers confidence of the prediction when assessing how to penalize incorrect classification.,What is log loss and how it helps to improve performance,7525
424,3191,"OLS (linear regression, linear model) assumes normally distributed residuals.  Ordinary least squares assumes things like equal variance of the noise at every x location. Generalized least squares does not assume a diagonal co-variance matrix.",Regression statistics What is the difference between Ordinary least square and generalized least squares,3191
425,2916,"First, make a list of the possible outcomes for each flip. Next, count the number of the possible outcomes for each flip. There are two outcomes for each flip of a coin: heads or tails. Then, multiply the number of outcomes by the number of flips.",How do you find the outcome of a sample space,2916
426,3962,"To say it informally, the filter size is how many neighbor information you can see when processing the current layer. When the filter size is 3*3, that means each neuron can see its left, right, upper, down, upper left, upper right, lower left, lower right, as a total of 8 neighbor information.",What is filter size in CNN,3962
427,7824,"Use In Exponential Distributions It is defined as the reciprocal of the scale parameter and indicates how quickly decay of the exponential function occurs. When the rate parameter = 1, there is no decay. Values close to 1 (e.g. 0.8 or 0.9) indicate a slow decay.",What is exponential distribution rate,7824
428,10777,"A rank-2 tensor gets two rotation matrices. This pattern generalizes to tensors of arbitrary rank. In a particular coordinate system, a rank-2 tensor can be expressed as a square matrix, but one should not marry the concepts of tensors and matrices, just like one should think of vectors simply as arrays of numbers.",What is a rank 2 tensor,10777
429,2823,Intelligence Quotient,What does IQ mean,2823
430,3178,"1 Answer. In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors.  If two authors generally use the same words then their vector will be closer.",What is the difference between word2vec and Doc2Vec,3178
431,690,"A ""single-layer"" perceptron can't implement XOR. The reason is because the classes in XOR are not linearly separable. You cannot draw a straight line to separate the points (0,0),(1,1) from the points (0,1),(1,0). Led to invention of multi-layer networks.",Why can t Perceptron learn XOR,690
432,8489,"To convert a frequency distribution to a probability distribution, divide area of the bar or interval of x by the total area of all the Bars. A simpler formula is: , N is the total Frequency and w is the interval of x.",How do you construct a probability distribution from a frequency distribution,8489
433,1627,A lazy learner delays abstracting from the data until it is asked to make a prediction while an eager learner abstracts away from the data during training and uses this abstraction to make predictions rather than directly compare queries with instances in the dataset.,What is the main difference between lazy and eager learning methods in nearest neighbor clustering,1627
434,863,1950s,When did artificial intelligence start,863
435,7668,"Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters).",How does Lasso regression work,7668
436,3917,"One tool they can use to do so is a decision tree. Decision trees are flowchart graphs or diagrams that help explore all of the decision alternatives and their possible outcomes.  Decision tree software helps businesses draw out their trees, assigns value and probabilities to each branch and analyzes each option.",How do decision trees help business decision making,3917
437,2658,"Negentropy is reverse entropy. It means things becoming more in order. By 'order' is meant organisation, structure and function: the opposite of randomness or chaos. One example of negentropy is a star system such as the Solar System.  The opposite of entropy is negentropy.",What is the opposite of entropy,2658
438,5458,"You can convert measures from discrete to continuous or from continuous to discrete. Click the field and choose Discrete or Continuous. The field is green when it is continuous, and blue when it is discrete. For measures in the Data pane, right-click the field and choose Convert to Discrete or Convert to Continuous.",How do you convert discrete data to continuous data,5458
439,4806,"A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a nonlinear model is more appropriate.",How do you explain a residual plot,4806
440,3804,"If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound. All predictive modeling problems have prediction error.",What is a good accuracy for machine learning model,3804
441,8314,You can regularize your network by introducing a dropout layer soon after the convolution layer. So a typical layer of Conv->Relu becomes Conv->Dropout->Relu. You may play around with the architecture rather than simply use pre-defined ones like VGG or AlexNet.,How is regularization implemented in the VGGNet 16 network,8314
442,1281,"Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.",What is a logistic regression used for,1281
443,4173,"On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.",What is on policy and off policy,4173
444,1878,"The converse of Theorem 1 is the following: Given vector field F = Pi + Qj on D with C1 coefficients, if Py = Qx, then F is the gradient of some function.",How do you tell if a vector field is a gradient field,1878
445,2502,Equality of result- making certain that people achieve the same result. An example is making sure that all students get the same grade no matter the race. Equality of opportunity- giving people an equal chance to succeed.,What is the difference between equality of opportunity and equality of results quizlet,2502
446,8558,FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment.  (You calculate one P-value for each sample or test in your experiment.),How is FDR calculated,8558
447,9116,"The first postulate of statistical mechanics � This postulate is often called the principle of equal a priori probabilities. It says that if the microstates have the same energy, volume, and number of particles, then they occur with equal frequency in the ensemble.",What is equal a priori probability,9116
448,907,"The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. Here are some good resources for Python: CodeAcademy.",Where do I start with artificial intelligence,907
449,1050,"Since most natural phenomena are complex and have many factors, the same logic as above applies and distribution of measures of such phenomena tend to have most values near the mean (normal distibution has a desirable property of mean and mode being the same - i.e. the mean is the same as the most frequent value).",Why does the normal distribution show up so often in nature,1050
450,6332,Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data.,How do you organize data for machine learning,6332
451,2619,"One way to par- allelize neural network training is to use a technique called Network Parallel Training (NPT). In this approach the neu- rons of the ANN are divided across machines in the cluster, so that each machine holds a portion of the neural network.",How do you parallelize neural network training,2619
452,5415,"SummaryWeighted Mean: A mean where some values contribute more than others.When the weights add to 1: just multiply each weight by the matching value and sum it all up.Otherwise, multiply each weight w by its matching value x, sum that all up, and divide by the sum of weights: Weighted Mean = ΣwxΣw.",How do you work out the weighted mean,5415
453,4133,"In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups.  Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers).",What is randomization experiment,4133
454,8802,"An interpolated string is a string literal that might contain interpolation expressions. When an interpolated string is resolved to a result string, items with interpolation expressions are replaced by the string representations of the expression results.",What does string interpolation mean,8802
455,382,The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement.,What is a correlation coefficient in simple words,382
456,10826,"The work efficiency formula is efficiency = output / input, and you can multiply the result by 100 to get work efficiency as a percentage. This is used across different methods of measuring energy and work, whether it's energy production or machine efficiency.",How do we calculate efficiency,10826
457,1296,"The 95% confidence interval (CI) is a range of values calculated from our data, that most likely, includes the true value of what we're estimating about the population.",What is confidence interval in machine learning,1296
458,571,"Linear Regression Is Limited to Linear Relationships By its nature, linear regression only looks at linear relationships between dependent and independent variables. That is, it assumes there is a straight-line relationship between them. Sometimes this is incorrect.",What is the common problem with linear regression,571
459,5366,"SYNONYMS FOR outlier 2 nonconformist, maverick; original, eccentric, bohemian; dissident, dissenter, iconoclast, heretic; outsider.",What is another word for outlier,5366
460,2206,The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.,What is hog computer vision,2206
461,2289,To get a p-value we compare our observed test- statistic to the randomization distribution of test- statistics obtained by assuming the null is true. The p-value will be the proportion of test- statistics in the randomization distribution that are as or more extreme than the observed test- statistic.,How do you find the p value for a randomization test,2289
462,9582,"5 Techniques to Prevent Overfitting in Neural NetworksSimplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model.  Early Stopping. Early stopping is a form of regularization while training a model with an iterative method, such as gradient descent.  Use Data Augmentation.  Use Regularization.  Use Dropouts.",What steps can we take to prevent Overfitting in a neural network,9582
463,3266,"The questionable cause—also known as causal fallacy, false cause, or non causa pro causa (""non-cause for cause"" in Latin)—is a category of informal fallacies in which a cause is incorrectly identified. For example: ""Every time I go to sleep, the sun goes down.",What is an example of a false cause fallacy,3266
464,8316,"So year is a discretized measure of a continuous interval variable, so quantitative.",Is year a quantitative variable,8316
465,9495,Usually a pattern recognition system uses training samples from known categories to form a decision rule for unknown patterns.  Clustering methods simply try to group similar patterns into clusters whose members are more similar to each other (according to some distance measure) than to members of other clusters.,What is clustering in pattern recognition,9495
466,753,Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma. Stemming is important in natural language understanding (NLU) and natural language processing (NLP).  Stemming is also a part of queries and Internet search engines.,What is stemming in NLP,753
467,7658,"S-Curves are used to visualize the progress of a project over time. They plot either cumulative work, based on person-hours, or costs over time. The name is derived from the fact that the data usually takes on an S-shape, with slower progress at the beginning and end of a project.",What is S curve used for,7658
468,1181,"The Cox proportional-hazards model (Cox, 1972) is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables.",What is Cox regression survival analysis,1181
469,312,"Qualitative Differences The population standard deviation is a parameter, which is a fixed value calculated from every individual in the population. A sample standard deviation is a statistic. This means that it is calculated from only some of the individuals in a population.",Is population standard deviation the same as standard deviation,312
470,5534,"In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable.  For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs.",What is bivariate variable,5534
471,8186,"The correlation structure between the dependent variables provides additional information to the model which gives MANOVA the following enhanced capabilities: Greater statistical power: When the dependent variables are correlated, MANOVA can identify effects that are smaller than those that regular ANOVA can find.",Why use a Manova instead of Anova,8186
472,7855,"Regression attempts to establish how X causes Y to change and the results of the analysis will change if X and Y are swapped. With correlation, the X and Y variables are interchangeable.  Correlation is a single statistic, whereas regression produces an entire equation.",What is the univariate correlation matrix Is it different from the Pearson correlation analysis,7855
473,8895,A variable xj is said to be endogenous within the causal model M if its value is determined or influenced by one or more of the independent variables X (excluding itself). A purely endogenous variable is a factor that is entirely determined by the states of other variables in the system.,How do you identify endogenous variables,8895
474,3159,"We propose that especially in the context of introducing automated decision aids to explicitly reduce human error, people become primed to use decision aids in biased ways. Rather than necessarily leading to fewer errors, automated decision aids may simply lead to di!erent kinds or classes of errors.",Does automation bias decision making,3159
475,8288,"Ridge regression has two main benefits. First, adding a penalty term reduces overfitting. Second, the penalty term guarantees that we can find a solution. I think the second part is easier to explain.",What are the benefits of using ridge regression over ordinary linear regression,8288
476,1349,"A recurrent neural network is shown one input each timestep and predicts one output. Conceptually, BPTT works by unrolling all input timesteps. Each timestep has one input timestep, one copy of the network, and one output. Errors are then calculated and accumulated for each timestep.",How does backpropagation work in RNN,1349
477,10751,"Word embeddings are widely used nowadays in Distributional Semantics and for a variety of tasks in NLP. Embeddings can be evaluated using ex- trinsic evaluation methods, i.e. the trained em- beddings are evaluated on a specific task such as part-of-speech tagging or named-entity recogni- tion (Schnabel et al., 2015).",How are word Embeddings usually evaluated,10751
478,5035,"A negative binomial distribution is concerned with the number of trials X that must occur until we have r successes. The number r is a whole number that we choose before we start performing our trials. The random variable X is still discrete. However, now the random variable can take on values of X = r, r+1, r+2,",How do you know if a binomial distribution is negative,5035
479,3610,"Put simply, batch processing is the process by which a computer completes batches of jobs, often simultaneously, in non-stop, sequential order. It's also a command that ensures large jobs are computed in small parts for efficiency during the debugging process.",What is the meaning of batch processing,3610
480,6326,"Convolution has applications that include probability, statistics, computer vision, natural language processing, image and signal processing, engineering, and differential equations.",What are the applications of convolution,6326
481,9680,"For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.",What is machine learning examples,9680
482,3698,"Verify that the partial derivative Fxy is correct by calculating its equivalent, Fyx, taking the derivatives in the opposite order (d/dy first, then d/dx). In the above example, the derivative d/dy of the function f(x,y) = 3x^2*y - 2xy is 3x^2 - 2x.",How do you find the partial derivative of fxy,3698
483,10540,We can reduce the size of a Tensorflow Model using the below mentioned methods: Freezing: Convert the variables stored in a checkpoint file of the SavedModel into constants stored directly in the model graph. This reduces the overall size of the model.,How do I reduce the size of a TensorFlow model,10540
484,1406,"In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable.  The Shannon information can be interpreted as quantifying the level of ""surprise"" of a particular outcome.",What does information content mean,1406
485,7348,"AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.",What is a B testing and how does it work,7348
486,8335,"An odds ratio is a measure of association between the presence or absence of two properties.  The value of the odds ratio tells you how much more likely someone under 25 might be to make a claim, for example, and the associated confidence interval indicates the degree of uncertainty associated with that ratio.",What is odds ratio and confidence interval,8335
487,1402,"Deep Neural Networks (DNN) have greater capabilities for image pattern recognition and are widely used in Computer Vision algorithms. And, Convolutional Neural Network (CNN, or ConvNet) is a class of DNN which is most commonly applied to analyzing visual imagery.",Why convolutional neural networks are preferred for computer vision applications,1402
488,7985,"In statistics, a Poisson distribution is a statistical distribution that shows how many times an event is likely to occur within a specified period of time. It is used for independent events which occur at a constant rate within a given interval of time.",What is the Poisson distribution used for,7985
489,7314,"Quota sampling means to take a very tailored sample that's in proportion to some characteristic or trait of a population.  For example, if your population consists of 45% female and 55% male, your sample should reflect those percentages.",What is quota sampling and example,7314
490,8283,"In general, K-means is a heuristic algorithm that partitions a data set into K clusters by minimizing the sum of squared distance in each cluster.  In this paper, the simulation of basic k-means algorithm is done, which is implemented using Euclidian distance metric.",What is the distance metric used in the standard implementation of K means to calculate the cluster assignments,8283
491,526,"Divide the total by the number of members of the cluster. In the example above, 283 divided by four is 70.75, and 213 divided by four is 53.25, so the centroid of the cluster is (70.75, 53.25).",How do you find the centroid in statistics,526
492,8318,"The Binomial Theorem: Formulas. The Binomial Theorem is a quick way (okay, it's a less slow way) of expanding (or multiplying out) a binomial expression that has been raised to some (generally inconveniently large) power. For instance, the expression (3x – 2)10 would be very painful to multiply out by hand.",How does the binomial theorem work,8318
493,6966,"There are several approaches to avoiding overfitting in building decision trees.Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set.Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.",How do you solve overfitting in decision tree,6966
494,3548,n_estimators : This is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower.,What is N_estimators in random forest,3548
495,9285,"The item response theory (IRT), also known as the latent response theory refers to a family of mathematical models that attempt to explain the relationship between latent traits (unobservable characteristic or attribute) and their manifestations (i.e. observed outcomes, responses or performance).",What is IRT model,9285
496,5926,"The hazard function is not a density or a probability. However, we can think of it as the probability of failure in an infinitesimally small time period between y and y + ∂y given that the subject has survived up till time y.",Is the hazard function a probability,5926
497,3454,"A rule-based system (e.g., production system, expert system) uses rules as the knowledge representation. These rules are coded into the system in the form of if-then-else statements.  So, let's regard rule-based systems as the simplest form of AI.",How does rule based AI model work,3454
498,9865,0:002:44Suggested clip · 118 secondsGeometric Distribution: Mean - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you find the mean of a geometric distribution,9865
499,9033,"Augmented reality holds the promise of creating direct, automatic, and actionable links between the physical world and electronic information. It provides a simple and immediate user interface to an electronically enhanced physical world.",What is the scope of augmented reality,9033
500,1353,Alternate-form reliability is the consistency of test results between two different – but equivalent – forms of a test. Alternate-form reliability is used when it is necessary to have two forms of the same tests.  – Alternative-form reliability is needed whenever two test forms are being used to measure the same thing.,What is alternate form of reliability,1353
501,7349,Recall quantifies the number of positive class predictions made out of all positive examples in the dataset. F-Measure provides a single score that balances both the concerns of precision and recall in one number.,What is the recall score for the machine learning model,7349
502,4676,"While precision refers to the percentage of your results which are relevant, recall refers to the percentage of total relevant results correctly classified by your algorithm. Unfortunately, it is not possible to maximize both these metrics at the same time, as one comes at the cost of another.",How do you interpret precision and recall,4676
503,2002,"According to Bezdek (1994), Computational Intelligence is a subset of Artificial Intelligence. There are two types of machine intelligence: the artificial one based on hard computing techniques and the computational one based on soft computing methods, which enable adaptation to many situations.",What is the difference between computational intelligence and artificial intelligence,2002
504,570,The accuracy is a measure of the degree of closeness of a measured or calculated value to its actual value. The percent error is the ratio of the error to the actual value multiplied by 100. The precision of a measurement is a measure of the reproducibility of a set of measurements.  A systematic error is human error.,Does percent error measure accuracy or precision explain,570
505,9498,"Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. For example, predictive modeling could help identify customers who are likely to purchase our new One AI software over the next 90 days.",Is predictive modeling AI,9498
506,2729,"Computer vision, however, is more than machine learning applied. It involves tasks as 3D scene modeling, multi-view camera geometry, structure-from-motion, stereo correspondence, point cloud processing, motion estimation and more, where machine learning is not a key element.",Is computer vision part of machine learning,2729
507,821,"A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes.",What is false negative in a classification table,821
508,4895,Use Regression to Analyze a Wide Variety of Relationships Include continuous and categorical variables. Use polynomial terms to model curvature. Assess interaction terms to determine whether the effect of one independent variable depends on the value of another variable.,When should regression analysis be performed,4895
509,10104,"The sample mean is a consistent estimator for the population mean. A consistent estimate has insignificant errors (variations) as sample sizes grow larger.  In other words, the more data you collect, a consistent estimator will be close to the real population parameter you're trying to measure.",Why is the definition of a consistent estimator the way it is,10104
510,8539,"A Correlation of 0 means that there is no linear relationship between the two variables. We already know that if two random variables are independent, the Covariance is 0. We can see that if we plug in 0 for the Covariance to the equation for Correlation, we will get a 0 for the Correlation.",What does a covariance of 0 mean,8539
511,637,"At a higher level, the chief difference between the L1 and the L2 terms is that the L2 term is proportional to the square of the β values, while the L1 norm is proportional the absolute value of the values in β.",What is the difference between l1 and l2 norms,637
512,7069,"The Rabin-Karp algorithm makes use of hash functions and the rolling hash technique. A hash function is essentially a function that maps one thing to a value. In particular, hashing can map data of arbitrary size to a value of fixed size.",Which technique is used in Rabin Karp algorithm,7069
513,10177,"“Candidate Sampling” training methods involve constructing a training task in which for each. training example. , we only need to evaluate. for a small set of candidate classes.",What is candidate sampling in machine learning,10177
514,2760,Static final variables 2) The variable MY_VAR is public which means any class can use it. It is a static variable so you won't need any object of class in order to access it. It's final so the value of this variable can never be changed in the current or in any class.,Can we change the value of static variable,2760
515,1393,The Poisson distribution is used to model the number of events occurring within a given time interval. λ is the shape parameter which indicates the average number of events in the given time interval. The following is the plot of the Poisson probability density function for four values of λ.,What is the parameter of the Poisson distribution,1393
516,10164,"Use the hypergeometric distribution with populations that are so small that the outcome of a trial has a large effect on the probability that the next outcome is an event or non-event. For example, in a population of 10 people, 7 people have O+ blood.",When would you use a hypergeometric distribution,10164
517,4582,"A training dataset is a dataset of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier.",What is training set in machine learning,4582
518,2446,"Just as correlation measures the extent of a linear relationship between two variables, autocorrelation measures the linear relationship between lagged values of a time series. There are several autocorrelation coefficients, corresponding to each panel in the lag plot.",What is the autocorrelation for a time series,2446
519,2346,"Association Rule Mining, as the name suggests, association rules are simple If/Then statements that help discover relationships between seemingly independent relational databases or other data repositories. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical.",What are association rules in data mining,2346
520,7190,"Generally, you're evidently not an AI, if we are talking about the computers and algorithms and codes. You cannot prove this topic unless you definitely define what is artificial intelligence and what you are. Generally, you're evidently not an AI, if we are talking about the computers and algorithms and codes.",How do you prove that you are not an artificial intelligence,7190
521,6524,"As far as i read in the manual, stream length is simply the number (n) of the sequent number of the random number sample. The bitstream is likely a number of sample size.",What does Stream length in NIST randomness test mean,6524
522,6571,"The Central limit Theorem states that when sample size tends to infinity, the sample mean will be normally distributed. The Law of Large Number states that when sample size tends to infinity, the sample mean equals to population mean.",What is the difference between law of large numbers and central limit theorem,6571
523,2449,"TL; DR: The naive Bayes classifier is an approximation to the Bayes classifier, in which we assume that the features are conditionally independent given the class instead of modeling their full conditional distribution given the class. A Bayes classifier is best interpreted as a decision rule.",What is the difference between the Naive Bayes Classifier and the Bayes classifier,2449
524,1452,"A histogram is drawn like a bar chart, but often has bars of unequal width. It is the area of the bar that tells us the frequency in a histogram, not its height. Instead of plotting frequency on the y-axis, we plot the frequency density. To calculate this, you divide the frequency of a group by the width of it.",What does density mean in histogram,1452
525,4750,Plot a symbol at the median and draw a box between the lower and upper quartiles. Calculate the interquartile range (the difference between the upper and lower quartile) and call it IQ. The line from the lower quartile to the minimum is now drawn from the lower quartile to the smallest point that is greater than L1.,How do you calculate a box plot,4750
526,1290,We shall look at 5 popular clustering algorithms that every data scientist should be aware of.K-means Clustering Algorithm.  Mean-Shift Clustering Algorithm.  DBSCAN – Density-Based Spatial Clustering of Applications with Noise.  EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)More items•,What are the most popular clustering algorithms,1290
527,8940,"According to this link LDA is a generative classifier. Also, the motto of LDA is to model a discriminant function to classify.",Is linear discriminant analysis a generative model,8940
528,5472,"Supervised Learning deals with two main tasks Regression and Classification. Unsupervised Learning deals with clustering and associative rule mining problems. Whereas Reinforcement Learning deals with exploitation or exploration, Markov's decision processes, Policy Learning, Deep Learning and value learning.",What is the difference between supervised unsupervised and reinforcement learning,5472
529,10631,A finite population is a collection of objects or individuals that are objects of research that occupy a certain area. It clear boundaries that distinguish these population groups from other populations.,What is a finite population in statistics,10631
530,1857,"Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data.  Specifically, underfitting occurs if the model or algorithm shows low variance but high bias. Underfitting is often a result of an excessively simple model.",What is Overfitting and Underfitting in learning,1857
531,3844,A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.,What is a simple random sample in statistics,3844
532,5380,"This variance represents what the regression line cannot predict. It's equal to the sum of squared deviations of data points around predicted points, divided by N minus two. N is the number of data points in the scatterplot. Regression variance is based on differences between predicted data points and the mean of Y.",How do you find the variance of a regression model,5380
533,4880,"Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.",What is data science and where it is used,4880
534,6401,Common examples of algorithms with coefficients that can be optimized using gradient descent are Linear Regression and Logistic Regression.,What algorithms use gradient descent,6401
535,6909,"It is acknowledged that current tests do not measure IQ to a level of accuracy of one point: there is a margin of error, usually considered to be about five points either side of the obtained IQ, which should be taken into account when making a diagnosis of ID (The American Association on Mental Retardation 2002).",What is the margin of error of IQ tests,6909
536,2709,Appropriate Problems for Decision Tree LearningInstances are represented by attribute-value pairs.  The target function has discrete output values.  Disjunctive descriptions may be required.  The training data may contain errors.  The training data may contain missing attribute values.,What are the issues in decision tree learning,2709
537,2801,"Testing approach: The answers lie in the data set. In order to test a machine learning algorithm, tester defines three different datasets viz. Training dataset, validation dataset and a test dataset (a subset of training dataset).",How do you test machine learning models,2801
538,10976,"The standard deviation formula may look confusing, but it will make sense after we break it down.  Step 1: Find the mean.Step 2: For each data point, find the square of its distance to the mean.Step 3: Sum the values from Step 2.Step 4: Divide by the number of data points.Step 5: Take the square root.",How do you find the standard deviation between two sets of data,10976
539,9687,"One way that we calculate the predicted probability of such binary events (drop out or not drop out) is using logistic regression. Unlike regular regression, the outcome calculates the predicted probability of mutually exclusive event occuring based on multiple external factors.",What is predicted probability in logistic regression,9687
540,6912,Cluster cohesion: Measures the closeness of the objects within the same cluster. A “lower within-cluster” variation indicates good compactness or good clustering. The separation method is implied to measure how well a cluster is separated from other clusters.,Which measures the goodness of a cluster,6912
541,5816,"Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others.",What is the impact of AI,5816
542,211,An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components.,What is Expert System Shell in artificial intelligence,211
543,2668,"In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as i.i.d. or iid or IID.",What does independently and identically distributed mean,2668
544,6185,"In convolutional networks, multiple filters are taken to slice through the image and map them one by one and learn different portions of an input image. Imagine a small filter sliding left to right across the image from top to bottom and that moving filter is looking for, say, a dark edge.",What is convolutional filter,6185
545,7166,"The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!",Can the same variable be used as the dependent and Independent variable after a time lapse,7166
546,9399,"As mentioned in the context of the gradient theorem, a vector field F is conservative if and only if it has a potential function f with F=∇f. Therefore, if you are given a potential function f or if you can find one, and that potential function is defined everywhere, then there is nothing more to do.",How do you know if F is conservative vector field,9399
547,9905,How to Calculate VarianceFind the mean of the data set. Add all data values and divide by the sample size n.Find the squared difference from the mean for each data value. Subtract the mean from each data value and square the result.Find the sum of all the squared differences.  Calculate the variance.,How do I calculate the variance,9905
548,9656,"To recap, Logistic regression is a binary classification method. It can be modelled as a function that can take in any number of inputs and constrain the output to be between 0 and 1. This means, we can think of Logistic Regression as a one-layer neural network.",Is logistic regression a neural network,9656
549,5411,"Multiple regression formula is used in the analysis of relationship between dependent and multiple independent variables and formula is represented by the equation Y is equal to a plus bX1 plus cX2 plus dX3 plus E where Y is dependent variable, X1, X2, X3 are independent variables, a is intercept, b, c, d are slopes,",What is the formula for multiple regression,5411
550,10997,"Quantum fields are matter.  The simplest “practical” quantum field theory is quantum electromagnetism. In it, two fields exist: the electromagnetic field and the “electron field”. These two fields continuously interact with each other, energy and momentum are transferred, and excitations are created or destroyed.",What is a field in QFT,10997
551,9058,"For example, if the distribution of raw scores if normally distributed, so is the distribution of z-scores. The mean of any SND always = 0. The standard deviation of any SND always = 1. Therefore, one standard deviation of the raw score (whatever raw value this is) converts into 1 z-score unit.",Why does az score have a mean of 0 and standard deviation of 1,9058
552,6794,"In this case, convergence in distribution implies convergence in probability. We can state the following theorem: Theorem If Xn d→ c, where c is a constant, then Xn p→ c. Since Xn d→ c, we conclude that for any ϵ>0, we have limn→∞FXn(c−ϵ)=0,limn→∞FXn(c+ϵ2)=1.",How do you prove probability convergence,6794
553,1173,"Visualping is the newest, easiest and most convenient tool to monitor websites changes. Our Chrome app allows to monitor pages with only 1 click directly from the page you wish to monitor. Users receive an email when changes are detected but can also set up a Slack integration for team notifications.",What is Visualping,1173
554,10430,"Word vectors are simply vectors of numbers that represent the meaning of a word.  In simpler terms, a word vector is a row of real-valued numbers (as opposed to dummy numbers) where each point captures a dimension of the word's meaning and where semantically similar words have similar vectors.",What is vector representation of words,10430
555,10716,"When the two options are available, lemmatization will always be a better option than stemming.  But if you can apply a lemmatizer, it will always give you a better result, because lemmatizers rely on correct language data (dictionaries) to identify a word with its lemma.",Is it advisable to choose lemmatization over stemming in NLP,10716
556,4361,"If a confusion matrix threshold is at disposal, instead, we recommend the usage of the Matthews correlation coefficient over F1 score, and accuracy.  We decided to focus on accuracy and F1 score because they are the most common metrics used for binary classification in machine learning.",Is the Matthews correlation coefficient widely used in binary classifier quality assessment,4361
557,588,"Calculate bias by finding the difference between an estimate and the actual value. To find the bias of a method, perform many estimates, and add up the errors in each estimate compared to the real value. Dividing by the number of estimates gives the bias of the method.",How do you calculate bias,588
558,7835,"The probability of committing a type II error is equal to one minus the power of the test, also known as beta. The power of the test could be increased by increasing the sample size, which decreases the risk of committing a type II error.",What is the probability of a Type II error,7835
559,5158,The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement.  It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data.,What is bootstrap in machine learning,5158
560,5823,"To take your first steps down the artificial intelligence career path, hiring managers will likely require that you hold at least a bachelor's degree in mathematics and basic computer technology. However, for the most part, bachelor's degrees will only get you into entry-level positions.",What degree do I need to work with artificial intelligence,5823
561,8568,"Experimental probability is the actual result of an experiment, which may be different from the theoretical probability. Example: you conduct an experiment where you flip a coin 100 times. The theoretical probability is 50% heads, 50% tails. The actual outcome of your experiment may be 47 heads, 53 tails.",What are some examples of experimental probability,8568
562,7793,"Train Generative Adversarial Network (GAN)Load Training Data.Define Generator Network.Define Discriminator Network.Define Model Gradients, Loss Functions and Scores.Specify Training Options.Train Model.Generate New Images.More items",How do you train a generative adversarial network,7793
563,9563,"Gradient descent is an optimization algorithm that finds the optimal weights (a,b) that reduces prediction error. Step 2: Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value.",What are the steps for using a gradient descent algorithm,9563
564,8765,"The attention mechanism is a part of a neural architecture that enables to dynamically highlight relevant features of the input data, which, in NLP, is typically a sequence of textual elements. It can be applied directly to the raw input or to its higher level representation.",What is attention mechanism in NLP,8765
565,4057,"A Bernouilli distribution is a discrete probability distribution for a Bernouilli trial — a random experiment that has only two outcomes (usually called a “Success” or a “Failure”).  The expected value for a random variable, X, from a Bernoulli distribution is: E[X] = p. For example, if p = . 04, then E[X] = 0.4.",What is the mean of a Bernoulli distribution,4057
566,3161,"In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions (f and g) that produces a third function ( ) that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it.",What is the convolution of two functions,3161
567,6813,"Tensorflow is the most used library used in development of Deep Learning models.  Keras, on the other end, is a high-level API that is built on top of TensorFlow. It is extremely user-friendly and comparatively easier than TensorFlow.",What is the difference between keras and tensorflow,6813
568,7257,"Gaussian random variables and Gaussian random vectors (vectors whose components are jointly Gaussian, as defined later) play a central role in detection and estimation.  Jointly Gaussian random variables are completely described by their means and covariances, which is part of the simplicity of working with them.",What is a Gaussian vector,7257
569,7507,"Order Statistics Definition Order statistics are sample values placed in ascending order. The study of order statistics deals with the applications of these ordered values and their functions. Let's say you had three weights: X1 = 22 kg, X2 = 44 kg, and X3 = 12 kg.",What is order statistics and why do we use it,7507
570,726,1:085:00Suggested clip · 93 secondsInterpreting Hazard Ratios - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you interpret hazard ratios,726
571,9392,"An estimator of a given parameter is said to be unbiased if its expected value is equal to the true value of the parameter. In other words, an estimator is unbiased if it produces parameter estimates that are on average correct.",How do you show an estimator is unbiased,9392
572,9706,"Returns the inverse, or critical value, of the cumulative standard normal distribution. This function computes the critical value so that the cumulative distribution is greater than or equal to a pre-specified value.",What is the inverse of the standard normal cumulative distribution,9706
573,8243,"This term is used in statistics in its ordinary sense, but most frequently occurs in connection with samples from different populations which may or may not be identical. If the populations are identical they are said to be homogeneous, and by extension, the sample data are also said to be homogeneous.",What is a homogeneous group in statistics,8243
574,200,"Bayesian analysis, a method of statistical inference (named for English mathematician Thomas Bayes) that allows one to combine prior information about a population parameter with evidence from information contained in a sample to guide the statistical inference process.",What is Bayesian analysis used for,200
575,9719,Ordinal YouTubeStart of suggested clipEnd of suggested clip,How do you do regression on Likert scale data,9719
576,8276,"A spectrum is simply a chart or a graph that shows the intensity of light being emitted over a range of energies.  Spectra can be produced for any energy of light, from low-energy radio waves to very high-energy gamma rays. Each spectrum holds a wide variety of information.",What is a spectral,8276
577,3570,"Alternatively, general dimensionality reduction techniques are used such as:Independent component analysis.Isomap.Kernel PCA.Latent semantic analysis.Partial least squares.Principal component analysis.Multifactor dimensionality reduction.Nonlinear dimensionality reduction.More items",What are the different feature extraction techniques,3570
578,737,"In the context of CNN, a filter is a set of learnable weights which are learned using the backpropagation algorithm. You can think of each filter as storing a single template/pattern.  Filter is referred to as a set of shared weights on the input.",What is a filter in a CNN,737
579,7685,"Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.",Can naive Bayes be used for multiclass classification,7685
580,9280,Filters typically are applied to data in the data processing stage or the preprocessing stage. Filters enhance the clarity of the signal that's used for machine learning.,What is filtering in machine learning,9280
581,812,"The difference between the hypergeometric and the binomial distributions.  For the binomial distribution, the probability is the same for every trial. For the hypergeometric distribution, each trial changes the probability for each subsequent trial because there is no replacement.",What is the difference between binomial and hypergeometric distribution,812
582,6005,Inverse transform sampling is a method for generating random numbers from any probability distribution by using its inverse cumulative distribution F−1(x). Recall that the cumulative distribution for a random variable X is FX(x)=P(X≤x).,How do you do inverse transformation,6005
583,4253,"From Wikipedia, the free encyclopedia. In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables.",What is meant by constrained optimization,4253
584,2900,"You calculate the mean, say it's 10. You calculate the standard deviation: it's 12. That means that any number from 10 to 22 is within one standard deviation away from the mean. Now if your data are symmetric (say normal), any number from -2 to 10 is also within a standard deviation from the mean.",How do you find how many standard deviations away from the mean,2900
585,8740,"A moving average is a technique that calculates the overall trend in a data set. In operations management, the data set is sales volume from historical data of the company. This technique is very useful for forecasting short-term trends. It is simply the average of a select set of time periods.",What is moving average method of forecasting,8740
586,2115,"The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq)",Can the binomial distribution be approximated by a normal distribution,2115
587,3896,"Tensors are a type of data structure used in linear algebra, and like vectors and matrices, you can calculate arithmetic operations with tensors.",What are tensors used for,3896
588,3787,1. A pattern recognition technique that is used to categorize a huge number of data into different classes.,What is feature classification,3787
589,394,"One assumption of Poisson Models is that the mean and the variance are equal, but this assumption is often violated. This can be dealt with by using a dispersion parameter if the difference is small or a negative binomial regression model if the difference is large.",What can we do if our modelling assumption are violated in Poisson Regression Modelling,394
590,2369,"There are a number of equations that can generate an S curve, the most common is logistics function with the equation (in Excel notation): S(x) = (1/(1+exp(-kx))^a is the simple form of the equation, where the minimum value is 0 and the maximum value is 1, k and a both >0 and control the shape.",How do you calculate S curve,2369
591,10885,"Now, every textbook on linear algebra gives the following definition of a linear operator: an operator T: V—> W between two vector spaces V and W over the same field ! F is said to be linear if it satisfies the conditions of additivity, viz. T(u + v)=T(u)+T(v)",What makes an operator linear,10885
592,3811,"The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. A Binomial Distribution shows either (S)uccess or (F)ailure.",What is binomial distribution with example,3811
593,2778,"Detection accuracy as discussed in this section refers to the agreement between the emotional states detected by different sets of emotion measurement equipment (e.g., multiple modalities), one of which is being used as the “grounded truth” (i.e., standard) for determining the correct emotion.",What is detection accuracy,2778
594,7914,"The basic strength of inductive reasoning is its use in predicting what might happen in the future or in establishing the possibility of what you will encounter. The main weakness of inductive reasoning is that it is incomplete, and you may reach false conclusions even with accurate observations.",What are the advantages and disadvantages of using inductive reasoning,7914
595,9352,"Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables. For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke.",What is discriminant analysis example,9352
596,9644,"Now, for the differences… The Mann-Whitney U is a very simple test that makes almost no assumptions about any underlying distribution.  Because the K-S test can assume interval or higher level data, it is a more powerful statistical test than the MW-U, assuming that assumption is valid.",What are the differences between the Kolmogorov Smirnov test and the Mann Whitney U test,9644
597,63,(mathematics) A symbol representing a product over a set of terms.,What does ∏ mean,63
598,5738,The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems.,Is artificial intelligence a technology,5738
599,8551,A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process).,What is a null hypothesis in laymans terms,8551
600,272,"In a 2-by-2 table with cells a, b, c, and d (see figure), the odds ratio is odds of the event in the exposure group (a/b) divided by the odds of the event in the control or non-exposure group (c/d). Thus the odds ratio is (a/b) / (c/d) which simplifies to ad/bc.",How do you calculate odds ratio,272
601,809,"Action words, or action verbs, simply express an action. The action is something the subject of the sentence or clause is doing and includes sleeping, sitting, and napping-so even though there is no movement, there is still an action.",What are the action words,809
602,10802,Decision trees use multiple algorithms to decide to split a node in two or more sub-nodes.  Decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. The algorithm selection is also based on type of target variables.,How does a tree decide where to split,10802
603,4713,"There are two possible objectives in a discriminant analysis: finding a predictive equation for classifying new individuals or interpreting the predictive equation to better understand the relationships that may exist among the variables. In many ways, discriminant analysis parallels multiple regression analysis.",What are the objectives of discriminant analysis,4713
604,9966,"Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the",Is Random Forest ensemble learning,9966
605,1868,Here are some important considerations while choosing an algorithm.Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.  Accuracy and/or Interpretability of the output.  Speed or Training time.  Linearity.  Number of features.,How do I find the best machine learning algorithm,1868
606,2624,"For a good regression model, you want to include the variables that you are specifically testing along with other variables that affect the response in order to avoid biased results.  Cross-validation determines how well your model generalizes to other data sets by partitioning your data.",What makes a good regression model,2624
607,5979,"Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.",Why do we use AI,5979
608,10059,"If you have outliers, the best way is to use a clustering algorithm that can handle them. For example DBSCAN clustering is robust against outliers when you choose minpts large enough. Don't use k-means: the squared error approach is sensitive to outliers. But there are variants such as k-means-- for handling outliers.",How do clusters deal with outliers,10059
609,4490,"T = (X – μ) / [ σ/√(n) ]. This makes the equation identical to the one for the z-score; the only difference is you're looking up the result in the T table, not the Z-table. For sample sizes over 30, you'll get the same result.",What is the similarity between a Z score and a T score,4490
610,6956,"The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability.  P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1).",How do you find the joint pdf of two random variables,6956
611,2422,Machine Learning is a set of algorithms that parse data and learns from the parsed data and use those learnings to discover patterns of interest. Neural Network or Artificial Neural Network is one set of algorithms used in machine learning for modeling the data using graphs of Neurons.,What is the difference between machine learning and neural networks,2422
612,10383,"Use imputation for the missing values. When the response is missing, we can use a predictive model to predict the missing response, then create a new fully-observed dataset containing the predictions instead of the missing values, and finally re-estimate the predictive model in this expanded dataset.",How can I deal with missing values in a predictive model,10383
613,10684,Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of some stochastic process given noisy and/or partial observations.  The state-space model can be nonlinear and the initial state and noise distributions can take any form required.,What is filtered in particle filtering,10684
614,604,"Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. Panel data refers to samples of the same cross-sectional units observed at multiple points in time.",How is panel data different from cross sectional data,604
615,5690,"Alpha sets the standard for how extreme the data must be before we can reject the null hypothesis. The p-value indicates how extreme the data are.  If the p-value is greater than alpha (p > . 05), then we fail to reject the null hypothesis, and we say that the result is statistically nonsignificant (n.s.).",Are alpha level and P value the same,5690
616,7627,Compressed sensing addresses the issue of high scan time by enabling faster acquisition by measuring fewer Fourier coefficients. This produces a high-quality image with relatively lower scan time.,How does compressed sensing work,7627
617,5804,"Post-pruning (or just pruning) is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to improve complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects.",What is pruning in decision trees Why is it important,5804
618,754,The latent space is simply a representation of compressed data in which similar data points are closer together in space. Latent space is useful for learning data features and for finding simpler representations of data for analysis.,What are latent spaces in representation learning,754
619,10889,"Log-loss measures the accuracy of a classifier. It is used when the model outputs a probability for each class, rather than just the most likely class. Log-loss measures the accuracy of a classifier. It is used when the model outputs a probability for each class, rather than just the most likely class.",Why do we use log loss,10889
620,427,"The Bayesian approach permits the use of objective data or subjective opinion in specifying a prior distribution. With the Bayesian approach, different individuals might specify different prior distributions.  Bayesian methods have been used extensively in statistical decision theory (see statistics: Decision analysis).",How is Bayesian analysis used,427
621,3016,Most recent answer One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set.,How do you compare data with different sample sizes,3016
622,9178,14:3826:41Suggested clip · 115 secondsCanonical correlation using SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you interpret canonical correlation in SPSS,9178
623,10960,"Conclusion. Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it's sometimes easy not pay enough attention and use the same data in different steps of the pipeline.",Why is validation important in machine learning,10960
624,336,"Learning statistics means learning to communicate using the statistical language, solving statistical problems, drawing conclusions, and supporting conclusions by explaining the reasoning behind them. There are often different ways to solve a statistical problem.",What is the goal of learning statistics,336
625,686,"Mean Absolute Error (MAE) The MAE is a simple way to measure error magnitude. It consists on the average of the absolute differences between the predictions and the observed values. Th measure goes from 0 to infinite, being 0 the best value you can get.",What is the error measure used in reinforcement learning,686
626,6797,"One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture.",How do you address a vanishing gradient problem,6797
627,2319,"In an analogy to standard deviation, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation (RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance, known as the standard error.",Is RMSE the same as standard error,2319
628,1226,"The marks for a group of students before (pre) and after (post) a teaching intervention are recorded below: Marks are continuous (scale) data. Continuous data are often summarised by giving their average and standard deviation (SD), and the paired t-test is used to compare the means of the two samples of related data.",What statistical test to use to compare pre and post tests,1226
629,6270,"A feature detector is also referred to as a kernel or a filter. Intuitively, the matrix representation of the input image is multiplied element-wise with the feature detector to produce a feature map, also known as a convolved feature or an activation map.",What is feature detector in CNN,6270
630,10257,"A sequence of random variables X1, X2, X3, ⋯ converges in probability to a random variable X, shown by Xn p→ X, if limn→∞P(|Xn−X|≥ϵ)=0, for all ϵ>0.",How do you show convergence in probability,10257
631,10876,"The Kalman filter uses a system's dynamic model (e.g., physical laws of motion), known control inputs to that system, and multiple sequential measurements (such as from sensors) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using only one measurement",How does Kalman filter work,10876
632,3651,Fourier Methods in Signal Processing The Fourier transform and discrete-time Fourier transform are mathematical analysis tools and cannot be evaluated exactly in a computer. The Fourier transform is used to analyze problems involving continuous-time signals or mixtures of continuous- and discrete-time signals.,What is the use of Fourier transform in signal processing,3651
633,8649,"The classic machine learning procedure follows the scientific paradigm of induction and deduction. In the inductive step we learn the model from raw data (so called training set), and in the deductive step the model is applied to predict the behaviour of new data.",Is machine learning inductive or deductive,8649
634,9577,"Syllabus:Basic Data Structures: Arrays, Strings, Stacks, Queues.Asymptotic analysis (Big-O notation)Basic math operations (addition, subtraction, multiplication, division, exponentiation)Sqrt(n) primality testing.Euclid's GCD Algorithm.Basic Recursion.Greedy Algorithms.Basic Dynamic Programming.More items",What are the topics to be covered in algorithms and data structures,9577
635,6019,"The power of a hypothesis test is affected by three factors. Sample size (n). Other things being equal, the greater the sample size, the greater the power of the test.  The greater the difference between the ""true"" value of a parameter and the value specified in the null hypothesis, the greater the power of the test.",How does power affect sample size,6019
636,5227,"Abstract. Network representation learning aims to embed the vertexes in a network into low-dimensional dense representations, in which similar vertices in the network should have “close” representations (usually measured by cosine similarity or Euclidean distance of their representations).",What are network representations,5227
637,816,Ridge regression does not really select variables in the many predictors situation.  Both ridge regression and the LASSO can outperform OLS regression in some predictive situations – exploiting the tradeoff between variance and bias in the mean square error.,Can ridge regression be used for variable selection,816
638,1919,"The difference is a matter of design. In the test of independence, observational units are collected at random from a population and two categorical variables are observed for each unit.  In the goodness-of-fit test there is only one observed variable.",What is the difference between the chi square goodness of fit and independence tests,1919
639,8256,Population variance (σ2) tells us how data points in a specific population are spread out.  Here N is the population size and the xi are data points. μ is the population mean.,What is N in population variance,8256
640,731,"Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data.",How do you ensemble a model,731
641,9313,"Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you're trying to analyze, the features you include in your dataset can vary widely.",What are feature variables,9313
642,8404,"The function fX(x) gives us the probability density at point x. It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. Remember that P(x<X≤x+Δ)=FX(x+Δ)−FX(x).",How do you find probability density,8404
643,3935,This list of requirements prioritization techniques provides an overview of common techniques that can be used in prioritizing requirements.Ranking.  Numerical Assignment (Grouping)  MoScoW Technique.  Bubble Sort Technique.  Hundred Dollar Method.  Analytic Hierarchy Process (AHP)  Five Whys.,What techniques can be used to prioritize changes,3935
644,1407,5 years,How long until AI is smarter than humans,1407
645,3142,"Given two random variables X and Y, the correlation is scale and location invariant in the sense that cor(X,Y)=cor(XT,YT), if XT=a+bX, and YT=c+dY, and b and d have the same sign (either both positive or both negative).",Are correlation scales invariant,3142
646,6704,Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset.  Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features.,What is dimensionality reduction in machine learning,6704
647,5529,In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population.,What is fixed effect in panel data regression,5529
648,5079,"The Generative Adversarial Network, or GAN, is an architecture that makes effective use of large, unlabeled datasets to train an image generator model via an image discriminator model. The discriminator model can be used as a starting point for developing a classifier model in some cases.",Can Gan be used for classification,5079
649,8124,"Generally, a machine learning pipeline describes or models your ML process: writing code, releasing it to production, performing data extractions, creating training models, and tuning the algorithm. An ML pipeline should be a continuous process as a team works on their ML platform.",What is pipeline in machine learning,8124
650,2921,"Stochastic (from from Greek στόχος (stókhos) 'aim, guess'.) is any randomly determined process. In mathematics the terms stochastic process and random process are interchangeable.",What is meant by stochastic,2921
651,10110,The sum of a square matrix and its conjugate transpose. is Hermitian. The difference of a square matrix and its conjugate transpose. is skew-Hermitian.,What are Hermitian and skew Hermitian matrix,10110
652,72,A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes.  Random variables are often used in econometric or regression analysis to determine statistical relationships among one another.,What exactly is a random variable,72
653,2604,"Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data.",What is dimensionality reduction and explain why it is required,2604
654,3082,"When the standard deviation or the mean change, something unusual is happening. To detect such changes, for each upcoming point “p” we create of window from “p” to “p-100″. Then, we calculate the standard deviation and mean of this window. If it changes too much, an anomaly has been detected.",How would you find an anomaly in a distribution,3082
655,8812,"Pure serial correlation: occurs when the error terms are correlated and the regression equation is correctly specified. The most commonly assumed form of serial correlation is first-order serial correlation, in which one error term is a function of a previous error term.",What is pure serial correlation,8812
656,6424,"Correlated vs. A correlated subquery can be thought of as a filter on the table that it refers to, as if the subquery were evaluated on each row of the table in the outer query. An uncorrelated subquery has no such external column references.",What is the difference between correlated and uncorrelated subquery,6424
657,1778,"By Jim Frost 45 Comments. Heteroscedasticity means unequal scatter. In regression analysis, we talk about heteroscedasticity in the context of the residuals or error term. Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values.",What is homoscedasticity in linear regression,1778
658,1916,2:266:36Suggested clip · 120 secondsAn Easy Rule to Setting Up the Null & Alternate Hypotheses YouTubeStart of suggested clipEnd of suggested clip,How do you create a null and alternative hypothesis,1916
659,4638,"To convert a logit ( glm output) to probability, follow these 3 steps:Take glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) .",How do you convert logit to probability,4638
660,1595,"The distribution function , also called the cumulative distribution function (CDF) or cumulative frequency function, describes the probability that a variate takes on a value less than or equal to a number . The distribution function is sometimes also denoted. (Evans et al. 2000, p.",What does distribution function mean,1595
661,851,"(There are two red fours in a deck of 52, the 4 of hearts and the 4 of diamonds). Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs.  Joint probability is the probability of two events occurring simultaneously. The probability of event A and event B occurring together.",Can you point out the difference between joint probability and conditional probability,851
662,4437,"The normal distribution is a continuous probability distribution that is symmetrical on both sides of the mean, so the right side of the center is a mirror image of the left side.  The normal distribution is often called the bell curve because the graph of its probability density looks like a bell.",What is a normal curve in statistics,4437
663,9864,"Characteristics of a Poisson Distribution The probability that an event occurs in a given time, distance, area, or volume is the same. Each event is independent of all other events. For example, the number of people who arrive in the first hour is independent of the number who arrive in any other hour.",What are the properties of Poisson distribution,9864
664,5449,"One of the stages that SIFT uses is to create a pyramid of scales of the image.  The feature detector then works by finding features that have a peak response not only in the image space, but in scale space too. This means that it finds the scale of the image which the feature will produce the highest response.",Why are SIFT descriptors scale invariant,5449
665,2942,"The difference between multi-task learning and meta-learning is: in multitask learning, your goal would be to try to solve all of the training tasks shown in the gray box (on the left picture); whereas in meta-learning your goal is to use these training tasks in order to solve new tasks with a small amount of data, so",What is the difference between meta learning and multi task learning,2942
666,4434,Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together.,How do you know if an event is independent,4434
667,9786,"Running the ProcedureClick Transform > Recode into Different Variables.Double-click on variable CommuteTime to move it to the Input Variable -> Output Variable box. In the Output Variable area, give the new variable the name CommuteLength, then click Change.Click the Old and New Values button.  Click OK.",How do you convert continuous variables to categorical in SPSS,9786
668,10295,"The Q-Q plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a Normal or exponential.  If both sets of quantiles came from the same distribution, we should see the points forming a line that's roughly straight.",What does a QQ plot help you to test,10295
669,5237,"Big Data is defined as data that is huge in size. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Examples of Big Data generation includes stock exchanges, social media sites, jet engines, etc.",What is big data with examples,5237
670,2091,"The median is a measure of center (location) of a list of numbers.  This will be the median. If there are an even number on the list then average the n/2 and the (N + 2)/2 numbers. In general, the median is at position (n + 1)/2. If this position is a whole number then you have the median at that position in the list.",What is N 2 in median,2091
671,109,"Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. (",What are gradient boosting models,109
672,6752,"The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted ""Y"" and the independent variables are denoted by ""X"".",Is the explanatory variable The dependent variable,6752
673,2296,"When p is greater than 0.5, the distribution will be positively skewed (the peak will be on the left side of the distribution, with relatively fewer observations on the right).",When binomial distribution is positively skewed,2296
674,9343,"As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation.",How do you calculate a standard score,9343
675,7117,"RL is an increasingly popular technique for organizations that deal regularly with large complex problem spaces. Because RL models learn by a continuous process of receiving rewards and punishments on every action taken, it is able to train systems to respond to unforeseen environments .",Why is reinforcement important in learning,7117
676,5112,"There are four basic sequence learning problems: sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated.",Which of the following is part of the sequence learning problem,5112
677,8756,"Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive because they cannot occur at the same time, it's either a head appears or a tail appears.",How do you determine if an event is mutually exclusive,8756
678,5778,"The Spearman correlation is the same as the Pearson correlation, but it is used on data from an ordinal scale. Which situation would be appropriate for obtaining a phi-coefficient with a Pearson test?",What is the difference between the Pearson correlation and the Spearman correlation quizlet,5778
679,8015,"Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough. Specifically, underfitting occurs if the model or algorithm shows low variance but high bias.",What is underfitting in machine learning,8015
680,1943,"7 steps to improve your data structure and algorithm skillsStep 1: Understand Depth vs. Breadth.Step 2: Start the Depth-First Approach—make a list of core questions.Step 3: Master each data structure.Step 4: Spaced Repetition.Step 5: Isolate techniques that are reused. Isolate actual code blocks.Step 6: Now, it's time for Breadth.Step 7: Practice on paper.",How do you get really good at algorithms,1943
681,1589,"The receptive field size of a unit can be increased in a number of ways. One option is to stack more layers to make the network deeper, which increases the receptive field size linearly by theory, as each extra layer increases the receptive field size by the kernel size.",How do you increase receptive fields,1589
682,9924,"A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network and the amount of training it gets.  Similarly, a complex enough neural network can learn any function.",Which gives nonlinearity to a neural network,9924
683,8378,"Center: The center is not affected by sample size. The mean of the sample means is always approximately the same as the population mean µ = 3,500. Spread: The spread is smaller for larger samples, so the standard deviation of the sample means decreases as sample size increases.",What effect does the sample size have on the standard deviation of all possible sample means,8378
684,4094,"After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)",What is the meaning of a null hypothesis being rejected,4094
685,2350,The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1.,What is the difference between probit and logistic regression,2350
686,3391,"There is a layer of input nodes, a layer of output nodes, and one or more intermediate layers. The interior layers are sometimes called “hidden layers” because they are not directly observable from the systems inputs and outputs.",Why is it called hidden layer,3391
687,1752,Model calibration is the process of adjustment of the model parameters and forcing within the margins of the uncertainties (in model parameters and / or model forcing) to obtain a model representation of the processes of interest that satisfies pre-agreed criteria (Goodness-of-Fit or Cost Function).,What is model calibration,1752
688,8366,"A GLM consists of three components: A random component, A systematic component, and. A link function.",What are the three components of a generalized linear model,8366
689,4662,"A p-value that is calculated using an approximation to the true distribution is called an asymptotic p-value.  A p-value calculated using the true distribution is called an exact p-value. For large sample sizes, the exact and asymptotic p-values are very similar.",What is an exact p value,4662
690,4113,"Empirical Relationship between Mean, Median and Mode In case of a moderately skewed distribution, the difference between mean and mode is almost equal to three times the difference between the mean and median. Thus, the empirical mean median mode relation is given as: Mean – Mode = 3 (Mean – Median)",What is the relation between mean mode median,4113
691,4738,Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate).,What correlation means statistics,4738
692,9278,Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data.,What is a stratified sampling method,9278
693,9283,"ReLU is important because it does not saturate; the gradient is always high (equal to 1) if the neuron activates. As long as it is not a dead neuron, successive updates are fairly effective. ReLU is also very quick to evaluate.",Why do we use ReLU in CNN,9283
694,10268,The confidence interval (CI) is a range of values that's likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval.,What is a confidence interval in statistics,10268
695,10698,"As a rule of thumb, I'd say that SVMs are great for relatively small data sets with fewer outliers.  Also, deep learning algorithms require much more experience: Setting up a neural network using deep learning algorithms is much more tedious than using an off-the-shelf classifiers such as random forests and SVMs.",Is SVM deep learning,10698
696,4872,"To minimize or avoid performance bias, investigators can consider cluster stratification of patients, in which all patients having an operation by one surgeon or at one hospital are placed into the same study group, as opposed to placing individual patients into groups.",How can we prevent investigators bias,4872
697,9649,"Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).",What does regression explain,9649
698,5597,"Events A and B are independent if: knowing whether A occured does not change the probability of B. Mathematically, can say in tw. Page 1. Events A and B are independent if: knowing whether A occured does not change the probability of B.",What does it mean for two events A and B to be statistically independent,5597
699,5513,"An independent random variable is a random variable that doesn't have an effect on the other random variables in your experiment. In other words, it doesn't affect the probability of another event happening.",What does it mean for random variables to be independent,5513
700,6136,"For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users.",What are some examples of user information required by recommendation engines that use collaborative filtering,6136
701,33,Selection Sort in CExample of Selection Sort.Algorithm for Selection Sort:Step 1 − Set min to the first location.Step 2 − Search the minimum element in the array.Step 3 – swap the first location with the minimum value in the array.Step 4 – assign the second element as min.Step 5 − Repeat the process until we get a sorted array.More items•,How do you write an algorithm for a selection sort,33
702,1048,"Activation functions cannot be linear because neural networks with a linear activation function are effective only one layer deep, regardless of how complex their architecture is.  Therefore, nonlinear functions must be continuous and differentiable between this range.",Why activation functions are nonlinear in deep learning,1048
703,8246,"Precision refers to how close estimates from different samples are to each other. For example, the standard error is a measure of precision. When the standard error is small, estimates from different samples will be close in value; and vice versa.",What is sample precision,8246
704,4291,0:3910:15Suggested clip · 118 secondsConducting a Multiple Regression using Microsoft Excel Data YouTubeStart of suggested clipEnd of suggested clip,How do you run a regression with multiple variables,4291
705,4842,"For the alternative formulation, where X is the number of trials up to and including the first success, the expected value is E(X) = 1/p = 1/0.1 = 10. For example 1 above, with p = 0.6, the mean number of failures before the first success is E(Y) = (1 − p)/p = (1 − 0.6)/0.6 = 0.67.",What is the expected value of a geometric random variable,4842
706,7909,"Solve each equation to get a solution to the binomial. For x^2 - 9 = 0, for example, x - 3 = 0 and x + 3 = 0. Solve each equation to get x = 3, -3. If one of the equations is a trinomial, such as x^2 + 2x + 4 = 0, solve it using the quadratic formula, which will result in two solutions (Resource).",How do you solve a binomial equation,7909
707,6244,Two classes of digital filters are Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). The term 'Impulse Response' refers to the appearance of the filter in the time domain.  The mathematical difference between the IIR and FIR implementation is that the IIR filter uses some of the filter output as input.,What is FIR and IIR filter,6244
708,7079,Discriminant function analysis (DFA) is a statistical procedure that classifies unknown individuals and the probability of their classification into a certain group (such as sex or ancestry group). Discriminant function analysis makes the assumption that the sample is normally distributed for the trait.,What is a discriminant function analysis,7079
709,1338,"Mentor: Well, if the line is a good fit for the data then the residual plot will be random. However, if the line is a bad fit for the data then the plot of the residuals will have a pattern.",How can you tell if a residual plot is a good fit for the data,1338
710,5866,"The common application of indicators is the detection of end points of titrations. The colour of an indicator alters when the acidity or the oxidizing strength of the solution, or the concentration of a certain chemical species, reaches a critical range of values.",What is the function of the indicator,5866
711,6293,"The number of bootstrap samples can be indicated with B (e.g. if you resample 10 times then B = 10). A star next to a statistic, like s* or x̄* indicates the statistic was calculated by resampling. A bootstrap statistic is sometimes denoted with a T, where T*b would be the Bth bootstrap sample statistic T.",How does bootstrap determine sample size,6293
712,5238,"“The distinction between white label and private label are subtle,” he writes. “That's why these terms are so easily confused. Private label is a brand sold exclusively in one retailer, for example, Equate (WalMart). White label is a generic product, which is sold to multiple retailers like generic ibuprofen (Advil).”",Whats the difference between private label and white label,5238
713,3260,Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters.,What is maximum likelihood hypothesis in machine learning,3260
714,9619,"Advertisements. Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of the required value. For this algorithm to work properly, the data collection should be in a sorted form and equally distributed.",What is interpolation search in data structure,9619
715,9952,"The hierarchical cluster analysis follows three basic steps: 1) calculate the distances, 2) link the clusters, and 3) choose a solution by selecting the right number of clusters. First, we have to select the variables upon which we base our clusters.",How do you do a cluster analysis,9952
716,5282,Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function.  You start by defining the initial parameter's values and from there gradient descent uses calculus to iteratively adjust the values so they minimize the given cost-function.,How does gradient descent work,5282
717,9136,Abnormal BRCA1 and BRCA2 genes are found in 5% to 10% of all breast cancer cases in the United States. A study found that women with an abnormal BRCA1 gene had a worse prognosis than women with an abnormal BRCA2 gene 5 years after diagnosis.,Which is worse brca1 or brca2,9136
718,10471,"Under the batch processing model, a set of data is collected over time, then fed into an analytics system. In other words, you collect a batch of information, then send it in for processing. Under the streaming model, data is fed into analytics tools piece-by-piece. The processing is usually done in real time.",What is the difference between batch processing and stream processing,10471
719,6579,"Let A and G be the Arithmetic Means and Geometric Means respectively of two positive numbers a and b. Then, As, a and b are positive numbers, it is obvious that A > G when G = -√ab.  This proves that the Arithmetic Mean of two positive numbers can never be less than their Geometric Means.",What is the relation between arithmetic mean and geometric mean,6579
720,10143,"A dummy variable (aka, an indicator variable) is a numeric variable that represents categorical data, such as gender, race, political affiliation, etc.  For example, suppose we are interested in political affiliation, a categorical variable that might assume three values - Republican, Democrat, or Independent.",What is dummy variable given an example,10143
721,596,"In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the",What is probability density function in normal distribution,596
722,10819,The loss given default (LGD) is an important calculation for financial institutions projecting out their expected losses due to borrowers defaulting on loans. The expected loss of a given loan is calculated as the LGD multiplied by both the probability of default and the exposure at default.,What is loss given default formula,10819
723,6354,"There are numerous applications of integrals. Using technology such as computer software, internet sources, graphing calculators and smartphone apps can make solving integral problems easier. Some applications of integrals are: Displacement, which is the integral of velocity with respect to time.",What are the applications of integral calculus in different fields,6354
724,8228,"In a dataset a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set.",What is the difference between training and test dataset,8228
725,8244,"When your child sits the eleven plus exam, the number of questions answered correctly decides the ""Raw Score"". If there are more than one tests, the score may be the sum of the raw scores.  A standardized test score is calculated by translating the raw score into a completely different scale.",How is 11+ standardized calculated,8244
726,1332,"Example 1: Draw a box-and-whisker plot for the data set {3, 7, 8, 5, 12, 14, 21, 13, 18}.  The box part represents the interquartile range and represents approximately the middle 50% of all the data. The data is divided into four regions, which each represent approximately 25% of the data.",What is an example of a box plot,1332
727,2093,"The mean is the average of the numbers. It is easy to calculate: add up all the numbers, then divide by how many numbers there are. In other words it is the sum divided by the count.",How do we find average,2093
728,3341,"Univariate analysis, looking at single variables, is typically the first procedure one does when examining first time data.  The SPSS tools for looking at single variables include the following procedures: Frequencies, Descriptives and Explore all located under the Analyze menu.",What is univariate analysis in SPSS,3341
729,6312,"Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization.  Otherwise, you can run your logistic regression without any standardization treatment on the features.",Does logistic regression require feature scaling,6312
730,4777,The law of large numbers is a theorem from probability and statistics that suggests that the average result from repeating an experiment multiple times will better approximate the true or expected underlying result. The law of large numbers explains why casinos always make money in the long run.,Why does the law of large numbers work,4777
731,6640,When observed outcome of dependent variable can have multiple possible types then logistic regression will be multinomial.,When the observed outcome of dependent variable can have multiple possible types Then the logistic regression is,6640
732,827,The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters.  The resulting classifier is used to classify (using k = 1) the data and thereby produce an initial randomized set of clusters.,How K means algorithm works,827
733,189,"This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data.  You can think of the bias as a measure of how easy it is to get a node to fire.",What is bias value why it is used,189
734,2211,"Linear Regression Analysis consists of more than just fitting a linear line through a cloud of data points. It consists of 3 stages – (1) analyzing the correlation and directionality of the data, (2) estimating the model, i.e., fitting the line, and (3) evaluating the validity and usefulness of the model.",What are the steps in regression analysis,2211
735,10724,"There are various ways to modify a study design to actively exclude or control confounding variables (3) including Randomization, Restriction and Matching. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders.",How do you control a confounding variable in regression,10724
736,5766,"Analysis of covariance (ANCOVA) is a general linear model which blends ANOVA and regression.  Mathematically, ANCOVA decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance.",What does analysis of covariance mean,5766
737,4488,"Statistical Methods for Finding the Best Regression ModelAdjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values.  P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.More items•",How do you choose the best regression model in R,4488
738,3587,"There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.",How do you find K for K means,3587
739,10641,"The normal curve is called Mesokurtic curve. If the curve of a distribution is peaked than a normal or mesokurtic curve then it is referred to as a Leptokurtic curve. If a curve is less peaked than a normal curve, it is called as a Platykurtic curve. That's why kurtosis of normal distribution equal to three.",Why kurtosis of normal distribution is 3,10641
740,1403,"Spatial mining is the extraction of knowledge/spatial relationship and interesting measures that are not explicitly stored in spatial database. Temporal mining is the extraction of knowledge about occurrence of an event whether they follow Cyclic , Random ,Seasonal variations etc.",What is temporal and spatial data mining,1403
741,1032,"In the literal meaning of the terms, a parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, while a non-parametric test is one that makes no such assumptions.",What does a parametric test mean,1032
742,1631,Ordinary least squares assumes things like equal variance of the noise at every x location. Generalized least squares does not assume a diagonal co-variance matrix.,What is the difference between ordinary least squares and generalized least squares,1631
743,5313,Agents can be grouped into four classes based on their degree of perceived intelligence and capability :Simple Reflex Agents.Model-Based Reflex Agents.Goal-Based Agents.Utility-Based Agents.Learning Agent.,What are the different types of agents in artificial intelligence,5313
744,5026,"The T distribution is similar to the normal distribution, just with fatter tails. Both assume a normally distributed population. T distributions have higher kurtosis than normal distributions. The probability of getting values very far from the mean is larger with a T distribution than a normal distribution.",How is the t distribution related to the normal distribution,5026
745,9513,"In AI, the study on perception is mostly focused on the reproduction of human perception, especially on the perception of aural and visual signals. However, this is not necessarily the case since the perception mechanism of a computer system does not have to be identical to that of a human being.",What is the perception of AI,9513
746,1844,"There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc.",How do you determine learning rate,1844
747,971,Overfitting refers to a model that models the training data too well. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.,What is Overfitting in deep learning,971
748,3578,The normal distribution is a continuous probability distribution. This has several implications for probability. The total area under the normal curve is equal to 1. The probability that a normal random variable X equals any particular value is 0.,Is normal distribution continuous or discrete,3578
749,147,The general regression tree building methodology allows input variables to be a mixture of continuous and categorical variables. A decision tree is generated when each decision node in the tree contains a test on some input variable's value. The terminal nodes of the tree contain the predicted output variable values.,What is regression tree in machine learning,147
750,6114,"Improve your model accuracy by Transfer Learning.Loading data using python libraries.Preprocess of data which includes reshaping, one-hot encoding and splitting.Constructing the model layers of CNN followed by model compiling, model training.Evaluating the model on test data.Finally, predicting the correct and incorrect labels.",How can transfer learning improve accuracy,6114
751,7011,Steps for Making decision treeGet list of rows (dataset) which are taken into consideration for making decision tree (recursively at each nodes).Calculate uncertanity of our dataset or Gini impurity or how much our data is mixed up etc.Generate list of all question which needs to be asked at that node.More items•,How do you use the decision tree in machine learning,7011
752,246,"AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability.  By analogy, Higher the AUC, better the model is at distinguishing between patients with disease and no disease.",What is the difference between AUC and ROC,246
753,6361,Qualities of a Good Sampling FrameInclude all individuals in the target population.Exclude all individuals not in the target population.Includes accurate information that can be used to contact selected individuals.,How do you determine a sampling frame,6361
754,6891,Adam is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based in training data.,What is Adam Optimiser,6891
755,5927,"The joint probability is symmetrical, meaning that P(A and B) is the same as P(B and A). The calculation using the conditional probability is also symmetrical, for example: P(A and B) = P(A given B)",How do you find conditional probability from joint probability,5927
756,2872,"Generally, a machine learning pipeline describes or models your ML process: writing code, releasing it to production, performing data extractions, creating training models, and tuning the algorithm. An ML pipeline should be a continuous process as a team works on their ML platform.",What is a pipeline in machine learning,2872
757,935,"A relative frequency distribution shows the proportion of the total number of observations associated with each value or class of values and is related to a probability distribution, which is extensively used in statistics.",How do you describe the relative frequency distribution,935
758,8650,"In ideal conditions, facial recognition systems can have near-perfect accuracy. Verification algorithms used to match subjects to clear reference images (like a passport photo or mugshot) can achieve accuracy scores as high as 99.97% on standard assessments like NIST's Facial Recognition Vendor Test (FRVT).",How good is facial recognition,8650
759,6163,Blocks and strata are different. Blocking refers to classifying experimental units into blocks whereas stratification refers to classifying individuals of a population into strata. The samples from the strata in a stratified random sample can be the blocks in an experiment.,In Experimental Design what is the difference between blocking and stratified sampling,6163
760,6854,People also want to know what professions will be most in demand.  This is known as a reward function that will allow AI platforms to come to conclusions instead of arriving at a prediction. Reward Functions are used for reinforcement learning models. Reward Function Engineering determines the rewards for actions.,What is reward in reinforcement learning,6854
761,9741,"For a random variable yt, the unconditional mean is simply the expected value, E ( y t ) . In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ωt. A conditional mean model specifies a functional form for E ( y t | Ω t ) . .",What is a conditional mean in regression,9741
762,8279,"Exponential beta value is interpreted with the reference category, where the probability of the dependent variable will increase or decrease. In continuous variables, it is interpreted with one unit increase in the independent variable, corresponding to the increase or decrease of the units of the dependent variable.",What is beta in logistic regression,8279
763,4710,"The three different ways of feature extraction are horizontal direction, vertical direction and diagonal direction. Recognition rate percentage for vertical, horizontal and diagonal based feature extraction using feed forward back propagation neural network as classification phase are 92.69, 93.68, 97.80 respectively.",What are the feature extraction techniques in image processing,4710
764,9812,"Means and Variances of Random Variables: The mean of a discrete random variable, X, is its weighted average. Each value of X is weighted by its probability. To find the mean of X, multiply each value of X by its probability, then add all the products. The mean of a random variable X is called the expected value of X.",How do you find the discrete random variable,9812
765,837,"Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed.",What is prior probability in statistics,837
766,3458,"Answer. A negative path loading is basically the same as a negative regression coefficient. I.e., For a path loading from X to Y it is the predicted increase in Y for a one unit increase on X holding all other variables constant. So a negative coefficient just means that as X increases, Y is predicted to decrease.",What does a negative path coefficient mean,10418
767,459,"With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast.  Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes.",What is feature and label in machine learning,459
768,593,"Get startedPrepare your TensorBoard logs. (or download a sample from here).Upload the logs. Install the latest version of TensorBoard to use the uploader. $ pip install -U tensorboard.  View your experiment on TensorBoard. dev. Follow the link provided to view your experiment, or share it with others.",How do you share a TensorBoard,593
769,10442,"General Properties of Probability Distributions The sum of all probabilities for all possible values must equal 1. Furthermore, the probability for a particular value or range of values must be between 0 and 1. Probability distributions describe the dispersion of the values of a random variable.",What are the two properties of a probability distribution,10442
770,456,Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM).  SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.,What is SMO in machine learning,456
771,5815,A logarithmic scale (or log scale) is a way of displaying numerical data over a very wide range of values in a compact way—typically the largest numbers in the data are hundreds or even thousands of times larger than the smallest numbers.,What does a logarithmic scale look like,5815
772,10021,Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans.,What is explainable machine learning,10021
773,227,"is that maximin is in decision theory and game theory etc, a rule to identify the worst outcome of each possible option to find one's best (maximum payoff) play while minimax is in decision theory, game theory, etc a decision rule used for minimizing the maximum possible loss, or maximizing the minimum gain.",What is the difference between Minimax and Maximin,227
774,7635,"The normal distribution, commonly known as the bell curve, occurs throughout statistics. It is actually imprecise to say ""the"" bell curve in this case, as there are an infinite number of these types of curves. Above is a formula that can be used to express any bell curve as a function of x.",What is the equation of a bell curve,7635
775,10213,"At a bare minimum, collect around 1000 examples. For most ""average"" problems, you should have 10,000 - 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 - 1,000,000 examples.",How much data do you need for machine learning,10213
776,4088,"In a skewed distribution, the upper half and the lower half of the data have a different amount of spread, so no single number such as the standard deviation could describe the spread very well.",How does skew affect standard deviation,4088
777,2526,"For example, a random variable could be the outcome of the roll of a die or the flip of a coin. A probability distribution is a list of all of the possible outcomes of a random variable along with their corresponding probability values.",What is the difference between random variable and probability distribution,2526
778,10953,"A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth) and percentiles (hundredth).",What does a quantile mean,10953
779,10310,A loss function is used to optimize a machine learning algorithm. The loss is calculated on training and validation and its interpretation is based on how well the model is doing in these two sets.  An accuracy metric is used to measure the algorithm's performance in an interpretable way.,What is validation Loss and Validation accuracy,10310
780,2257,The standard normal or z-distribution assumes that you know the population standard deviation. The t-distribution is based on the sample standard deviation.,How does the t distribution differ from the z distribution,2257
781,477,"Sanderson points out in her book Social Psychology, confirmation bias also helps form and re-confirm stereotypes we have about people:3 ""We also ignore information that disputes our expectations.",What role does confirmation bias play in stereotyping,477
782,10890,"Logistic regression can be binomial, ordinal or multinomial. Binomial or binary logistic regression deals with situations in which the observed outcome for a dependent variable can have only two possible types, ""0"" and ""1"" (which may represent, for example, ""dead"" vs. ""alive"" or ""win"" vs. ""loss"").",What are the types of logistic regression,10890
783,2977,Mini-Max Algorithm in Artificial Intelligence. Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and game theory. It provides an optimal move for the player assuming that opponent is also playing optimally.  This Algorithm computes the minimax decision for the current state.,What is Minimax algorithm in AI,2977
784,835,6:3017:57Suggested clip · 93 secondsSAS - Logistic Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you run a logistic regression in SAS,835
785,8572,"Loss functions in neural networks The loss function is what SGD is attempting to minimize by iteratively updating the weights in the network. At the end of each epoch during the training process, the loss will be calculated using the network's output predictions and the true labels for the respective input.",What is loss in neural network training,8572
786,10847,"It does this by using a means of representing knowledge called, semantic networks. These use graphical methods to describe relationships between concepts and events to describe common sense activities.",How is common sense knowledge represented,10847
787,10992,"The neuron is the basic working unit of the brain, a specialized cell designed to transmit information to other nerve cells, muscle, or gland cells. Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells. Most neurons have a cell body, an axon, and dendrites.",What are the functions of neurons,10992
788,8987,Association between two variables means the values of one variable relate in some way to the values of the other. Association is usually measured by correlation for two continuous variables and by cross tabulation and a Chi-square test for two categorical variables.,What is an association between two variables,8987
789,536,"The training data is an initial set of data used to help a program understand how to apply technologies like neural networks to learn and produce sophisticated results.  Training data is also known as a training set, training dataset or learning set.",What is a training set in data mining,536
790,3986,"The range is the distance from the highest value to the lowest value. The Inter-Quartile Range is quite literally just the range of the quartiles: the distance from the largest quartile to the smallest quartile, which is IQR=Q3-Q1.",How do you compare the interquartile range and range,3986
791,7400,"The binomial theorem is an algebraic method of expanding a binomial expression. Essentially, it demonstrates what happens when you multiply a binomial by itself (as many times as you want). For example, consider the expression (4x+y)7 ( 4 x + y ) 7 .",What is binomial expansion method,7400
792,6435,1.96,What is the z value in Wilcoxon signed rank test,6435
793,9249,A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling.,Why Max pooling is used in CNN,9249
794,6303,"Repeating patterns often show serial correlation when the level of a variable affects its future level. In finance, this correlation is used by technical analysts to determine how well the past price of a security predicts the future price. Serial correlation is also known as autocorrelation or lagged correlation.",What does autocorrelation or serial correlation imply,6303
795,2366,"In unsupervised learning, there is no training data set and outcomes are unknown. Essentially the AI goes into the problem blind – with only its faultless logical operations to guide it.",Does unsupervised learning need training data,2366
796,8358,"The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq)",Can the normal distribution be used to approximate this probability,8358
797,10399,"Improving recall involves adding more accurately tagged text data to the tag in question. In this case, you are looking for the texts that should be in this tag but are not, or were incorrectly predicted (False Negatives). The best way to find these kind of texts is to search for them using keywords.",How do you increase recall in machine learning,10399
798,4512,Top 10 Data Analytics toolsR Programming. R is the leading analytics tool in the industry and widely used for statistics and data modeling.  Tableau Public:  SAS:  Apache Spark.  Excel.  RapidMiner:KNIME.  QlikView.More items•,What are the tools used in data analysis,4512
799,220,It is a rate per unit of time similar in meaning to reading a car speedometer at a particular instant and seeing 45 mph.  The failure rate (or hazard rate) is denoted by h(t) and is calculated from h(t) = \frac{f(t)}{1 - F(t)} = \frac{f(t)}{R(t)} = \mbox{the instantaneous (conditional) failure rate.},How are hazard rates calculated,220
800,6979,"The main difference is obviously that, in a first order reaction, the order of reaction is one by nature. A pseudo first-order reaction is second order reaction by nature but has been altered to make it a first order reaction.",What is the difference between first order and pseudo first order reactions,6979
801,241,8:3514:50Suggested clip · 95 secondsLecture 6.3 — Logistic Regression | Decision Boundary — [ Machine YouTubeStart of suggested clipEnd of suggested clip,How do you determine the decision boundary in logistic regression,241
802,8198,Some of the methods commonly used for binary classification are:Decision trees.Random forests.Bayesian networks.Support vector machines.Neural networks.Logistic regression.Probit model.,What are some binary classification algorithms,8198
803,2052,"As the name implies, multivariate regression is a technique that estimates a single regression model with more than one outcome variable. When there is more than one predictor variable in a multivariate regression model, the model is a multivariate multiple regression.",What is meant by multivariate regression analysis,2052
804,3623,"A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter being estimated. The following lists some types of biases, which can overlap. Selection bias involves individuals being more likely to be selected for study than others, biasing the sample.",What does it mean if a statistic is biased,3623
805,5736,"Cross Entropy is definitely a good loss function for Classification Problems, because it minimizes the distance between two probability distributions - predicted and actual.  So cross entropy make sure we are minimizing the difference between the two probability. This is the reason.",Why do we use cross entropy loss,5736
806,6008,SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.,Why is SVM used for image classification,6008
807,6180,"Neural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y).",Is neural style transfer supervised learning,6180
808,9780,"At a bare minimum, collect around 1000 examples. For most ""average"" problems, you should have 10,000 - 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 - 1,000,000 examples.",How many observations do you need for machine learning,9780
809,3691,"Both skew and kurtosis can be analyzed through descriptive statistics. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 when utilizing SEM (Brown, 2006).",What kurtosis is acceptable,3691
810,9252,"At a higher level, the chief difference between the L1 and the L2 terms is that the L2 term is proportional to the square of the β values, while the L1 norm is proportional the absolute value of the values in β.",What is the difference between l1 and l2 norm,9252
811,85,"While there are a number of different methods for measuring intelligence, the standard and most widely accepted method is by measuring a person's 'intelligence quotient' or IQ. Based on a series of tests which assess various types of abilities such a mathematical, spatial, verbal, logic and memory.",How do you assess your own level of intelligence,85
812,9855,"If a and b are two non-zero numbers, then the harmonic mean of a and b is a number H such that the numbers a, H, b are in H.P. We have H = 1/H = 1/2 (1/a + 1/b) ⇒ H = 2ab/a+b.",What is harmonic mean of A and B,9855
813,10648,A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable.  A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable.,What is the difference between a control variable and a control group,10648
814,2784,"In behavioral finance, base rate fallacy is the tendency for people to erroneously judge the likelihood of a situation by not taking into account all relevant data. Instead, investors might focus more heavily on new information without acknowledging how this impacts original assumptions.",What is the base rate fallacy and why is it important to avoid it,2784
815,448,"The obvious difference between ANOVA and ANCOVA is the the letter ""C"", which stands for 'covariance'. Like ANOVA, ""Analysis of Covariance"" (ANCOVA) has a single continuous response variable. ANCOVA is also commonly used to describe analyses with a single response variable, continuous IVs, and no factors.",What is the difference between Anova and Ancova tests,448
816,8453,"1 Answer. In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors.  If two authors generally use the same words then their vector will be closer.",How is Doc2Vec different from word2vec,8453
817,6228,"The base rate fallacy occurs when prototypical or stereotypical factors are used for analysis rather than actual data. Because the student is volunteering in a hospital with a stroke center, he sees more patients who have experienced a stroke than would be expected in a hospital without a stroke center.",What is base rate fallacy MCAT,6228
818,9921,6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case StudyGet a basic understanding of the algorithm.Find some different learning sources.Break the algorithm into chunks.Start with a simple example.Validate with a trusted implementation.Write up your process.,How do you create a learning algorithm,9921
819,2164,OLS cannot be used because the regression function is not a linear function of the regression coefficients (the coefficients appear inside the nonlinear functions Φ or Λ).,Why are the coefficients of probit and logit models estimated by maximum likelihood instead of OLS,2164
820,689,0:008:33Suggested clip · 112 secondsHow to read a log scale. - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you read a log scale,689
821,1177,The Poisson distribution has the following characteristics: It is a discrete distribution. Each occurrence is independent of the other occurrences. It describes discrete occurrences over an interval. The occurrences in each interval can range from zero to infinity.,What is Poisson distribution and its characteristics,1177
822,9877,"SVM Kernel Functions SVM algorithms use a set of mathematical functions that are defined as the kernel. The function of kernel is to take data as input and transform it into the required form.  For example linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.",What are SVM kernels,9877
823,3384,Parametric tests assume underlying statistical distributions in the data. Nonparametric tests do not rely on any distribution.  They can thus be applied even if parametric conditions of validity are not met.,What is the difference between parametric and nonparametric tests,3384
824,781,"In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each",How do you describe a categorical distribution,781
825,2017,In addition every algorithm must satisfy the following criteria:input: there are zero or more quantities which are externally supplied;output: at least one quantity is produced;definiteness: each instruction must be clear and unambiguous;More items,What are two important criteria for algorithms,2017
826,7671,"Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words.",What is the use of tokenization in NLP,7671
827,6089,"Yes, although 'linear regression' refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data.",Is OLS the same as linear regression,6089
828,2437,"The Boruta algorithm is a wrapper built around the random forest classification algorithm. It tries to capture all the important, interesting features you might have in your dataset with respect to an outcome variable. First, it duplicates the dataset, and shuffle the values in each column.",How does Boruta algorithm work,2437
829,8421,"Introduction[edit] Shift Invariance simply refers to the 'invariance' that a CNN has to recognising images. It allows the CNN to detect features/objects even if it does not look exactly like the images in it's training period. Shift invariance covers 'small' differences, such as movements shifts of a couple of pixels.",What is spatial invariance in CNN,8421
830,4549,fastText is a library for efficient learning of word representations and sentence classification. In this document we present how to use fastText in python.,What is fastText in Python,4549
831,1118,The SMD is preferable when the studies in a meta-analysis measure a given outcome using different scales or instruments.,What is the main advantage of the standardized mean difference SMD over the mean difference MD ),1118
832,7102,"A histogram is a graphical display of data using bars of different heights. In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data.",How does a histogram work,7102
833,952,"Unsupervised learning is very useful in exploratory analysis because it can automatically identify structure in data.  Dimensionality reduction, which refers to the methods used to represent data using less columns or features, can be accomplished through unsupervised methods.",Why unsupervised learning is important,952
834,5092,1. A numerical value that defines the learning capability of a neural network during training. Learn more in: Voltage Instability Detection Using Neural Networks.,What is learning coefficient,5092
835,2882,"Output is defined as the act of producing something, the amount of something that is produced or the process in which something is delivered. An example of output is the electricity produced by a power plant. An example of output is producing 1,000 cases of a product.",What output means,2882
836,4240,"To calculate the learnable parameters here, all we have to do is just multiply the by the shape of width m, height n, previous layer's filters d and account for all such filters k in the current layer. Don't forget the bias term for each of the filter.",How are learnable parameters calculated CNN,4240
837,7397,"ROC curves in logistic regression are used for determining the best cutoff value for predicting whether a new observation is a ""failure"" (0) or a ""success"" (1).  Your observed outcome in logistic regression can ONLY be 0 or 1. The predicted probabilities from the model can take on all possible values between 0 and 1.",What is ROC in logistic regression,7397
838,3892,"Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image.",What is image processing used for,3892
839,3806,Bayesian neural networks differ from plain neural networks in that their weights are assigned a probability distribution instead of a single value or point estimate. These probability distributions describe the uncertainty in weights and can be used to estimate uncertainty in predictions.,What are Bayesian neural networks,3806
840,2121,"Precision is a metric that quantifies the number of correct positive predictions made. Precision, therefore, calculates the accuracy for the minority class. It is calculated as the ratio of correctly predicted positive examples divided by the total number of positive examples that were predicted.",How do you find the accuracy of a precision and recall,2121
841,8504,"In an economic model, an exogenous variable is one whose value is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. In contrast, an endogenous variable is a variable whose value is determined by the model.",What is the difference between an exogenous variable and an endogenous variable,8504
842,8472,"The slope of a least squares regression can be calculated by m = r(SDy/SDx). In this case (where the line is given) you can find the slope by dividing delta y by delta x. So a score difference of 15 (dy) would be divided by a study time of 1 hour (dx), which gives a slope of 15/1 = 15.",How do you interpret the slope of the least squares regression line,8472
843,3209,"A hierarchical linear regression is a special form of a multiple linear regression analysis in which more variables are added to the model in separate steps called “blocks.” This is often done to statistically “control” for certain variables, to see whether adding variables significantly improves a model's ability to",What is a hierarchical regression analysis,3209
844,10322,"A non-convex optimization problem is any problem where the objective or any of the constraints are non-convex, as pictured below. Such a problem may have multiple feasible regions and multiple locally optimal points within each region.",What is Nonconvex optimization,10322
845,3029,"Enneagram test results are very accurate for determining your enneagram type and the MBTI test results are quite accurate for determining your MBTI type. Neither is in competition with the other. That being said, it can be very interesting to have the results for both of these uniquely different typologies.",Which is more accurate MBTI and Enneagram,3029
846,3188,"The odds ratio tells us how much higher the odds of exposure are among case-patients than among controls. An odds ratio of • 1.0 (or close to 1.0) indicates that the odds of exposure among case-patients are the same as, or similar to, the odds of exposure among controls. The exposure is not associated with the disease.",What does an odds ratio of 1.0 mean,3188
847,9831,"A statistic is a number that represents a property of the sample. For example, if we consider one math class to be a sample of the population of all math classes, then the average number of points earned by students in that one math class at the end of the term is an example of a statistic.",What is an example of an statistic,9831
848,546,1 Answer. 1. 8. Without math: The delta rule uses gradient descent to minimize the error from a perceptron network's weights. Gradient descent is a general algorithm that gradually changes a vector of parameters in order to minimize an objective function.,What is gradient descent and Delta Rule,546
849,7818,Types of predictive modelsForecast models. A forecast model is one of the most common predictive analytics models.  Classification models.  Outliers Models.  Time series model.  Clustering Model.  The need for massive training datasets.  Properly categorising data.,What are the types of predictive models,7818
850,5327,"The correct interpretation of a 95% confidence interval is that ""we are 95% confident that the population parameter is between X and X.""",How do you interpret a confidence interval,5327
851,8273,"Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions.  Log Loss quantifies the accuracy of a classifier by penalising false classifications.",What is log loss function,8273
852,5153,"In review, beta-endorphins are proteins that are primarily synthesized by the pituitary gland in response to physiologic stressors such as pain. They function through various mechanisms in both the central and peripheral nervous system to relieve pain when bound to their mu-opioid receptors.",What releases beta endorphins,5153
853,8525,Q17. Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection? “Ridge regression” will use all predictors in final model whereas “Lasso regression” can be used for feature selection because coefficient values can be zero.,Which of the following is true about Ridge or lasso regression methods in case of feature selection,8525
854,2881,"The optimal number of clusters can be defined as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square (wss). Plot the curve of wss according to the number of clusters k.",How do you define K in K means clustering,2881
855,2109,"5 Most Important Methods For Statistical Data AnalysisMean. The arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list.  Standard Deviation.  Regression.  Sample Size Determination.  Hypothesis Testing.",What are some of the statistical methods that are useful for data analyst,2109
856,4234,"Binary cross-entropy is for multi-label classifications, whereas categorical cross entropy is for multi-class classification where each example belongs to a single class.",What is the difference between binary cross entropy and categorical cross entropy,4234
857,8458,"The critical value is a factor used to compute the margin of error, as shown in the equations below. When the sampling distribution of the statistic is normal or nearly normal, the critical value can be expressed as a t score or as a z-score.",Is critical value the same as Z score,8458
858,9981,"Examples of such greedy algorithms are Kruskal's algorithm and Prim's algorithm for finding minimum spanning trees, and the algorithm for finding optimum Huffman trees. Greedy algorithms appear in network routing as well.",What is greedy algorithm example,9981
859,5229,"False positive rate (FPR) is a measure of accuracy for a test: be it a medical diagnostic test, a machine learning model, or something else. In technical terms, the false positive rate is defined as the probability of falsely rejecting the null hypothesis.",What does false positive rate mean,5229
860,5582,"The sample variance is an estimator for the population variance. When applied to sample data, the population variance formula is a biased estimator of the population variance: it tends to underestimate the amount of variability.  We are using one fitted value (sample mean) in our estimate of the variance.",Why is the formula for sample variance different from the formula for population variance,5582
861,2783,"The variance is the average of the squared differences from the mean. Standard deviation is the square root of the variance so that the standard deviation would be about 3.03.  Because of this squaring, the variance is no longer in the same unit of measurement as the original data.",Is sample variance and standard deviation the same,2783
862,10600,"Unlike the independent-samples t-test, the Mann-Whitney U test allows you to draw different conclusions about your data depending on the assumptions you make about your data's distribution.  These different conclusions hinge on the shape of the distributions of your data, which we explain more about later.",Why use Mann Whitney U test instead of t test,10600
863,9211,"The cumulative distribution function (CDF) of random variable X is defined as FX(x)=P(X≤x), for all x∈R.SolutionTo find the CDF, note that.  To find P(2<X≤5), we can write P(2<X≤5)=FX(5)−FX(2)=3132−34=732.  To find P(X>4), we can write P(X>4)=1−P(X≤4)=1−FX(4)=1−1516=116.",How do you find the CDF of a random variable,9211
864,649,"The central limit theorem applies to almost all types of probability distributions, but there are exceptions. For example, the population must have a finite variance. That restriction rules out the Cauchy distribution because it has infinite variance.",Does the central limit theorem apply to variance,649
865,9488,"The range containing values that are consistent with the null hypothesis is the ""acceptance region""; the other range, in which the null hypothesis is rejected, is the rejection region (or critical region).",What is the difference between critical region and acceptance region,9488
866,3013,"Data Augmentation in play. A convolutional neural network that can robustly classify objects even if its placed in different orientations is said to have the property called invariance. More specifically, a CNN can be invariant to translation, viewpoint, size or illumination (Or a combination of the above).",What is data augmentation in CNN,3013
867,9094,"A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.",What does a convolutional neural network do,9094
868,10624,"In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training.  Given these hyperparameters, the training algorithm learns the parameters from the data.",What are hyperparameters in machine learning,10624
869,3124,"There are two stages to prediction. The first stage is training the model—this is where the tree is built, tested, and optimized by using an existing collection of data. In the second stage, you actually use the model to predict an unknown outcome.",How do you make predictions in decision trees,3124
870,8928,"In two-dimensional signals like digital images, frequencies are rate of change of grey scale value (intensity of pixel) with respect to space. This is also called Spatial frequency .  Convert the cosine values represented by the red dots into greyscale (0-255), such that -1 maps to 0 and 1 maps to 255.",What is frequency of an image,8928
871,7244,"If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e.  For each choice of base, the logit function takes values between negative and positive infinity.",What is the logit function when P refers to probability of occurrence of an event,7244
872,5043,Artificial intelligence is probably the most widely-known for its application in the etail/retail industry. Conversation intelligence software helps companies interact with customers and follow up leads by analyzing and segmenting sales calls using speech recognition and natural language processing.,In which field is artificial intelligence used,5043
873,5959,"Classification and regression tree (CART) analysis recursively partitions observations in a matched data set, consisting of a categorical (for classification trees) or continuous (for regression trees) dependent (response) variable and one or more independent (explanatory) variables, into progressively smaller groups (",What is classification and regression tree analysis,5959
874,4076,"Systematic sampling involves selecting fixed intervals from the larger population to create the sample. Cluster sampling divides the population into groups, then takes a random sample from each cluster.",What are the differences between systematic random sampling and cluster sampling,4076
875,3057,"Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected.",What is prior probability in machine learning,3057
876,1908,"8 Common Data Structures every Programmer must know. A quick introduction to 8 commonly used data structures.  Arrays. An array is a structure of fixed-size, which can hold items of the same data type.  Linked Lists.  Stacks.  Queues.  Hash Tables.  Trees.  Heaps.More items",What are the topics in data structures,1908
877,1623,"Technically, all interpreters do the same thing and follow the same basic principles. But since sign languages are visual-manual while spoken languages are based on speaking, hearing and writing/reading, the difference entails several special requirements for interpreting.",In what ways are spoken languages and signed languages the same different,1623
878,6980,It is a criterion under which a hypothesis tester decides whether a given hypothesis must be accepted or rejected. The general rule of thumb is that if the value of test statics is greater than the critical value then the null hypothesis is rejected in the favor of the alternate hypothesis.,What is the rejection rule,6980
879,3071,"The basic premise of transfer learning is simple: take a model trained on a large dataset and transfer its knowledge to a smaller dataset. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction.",What is transfer learning in CNN,3071
880,3829,A significant advantage of a decision tree is that it forces the consideration of all possible outcomes of a decision and traces each path to a conclusion. It creates a comprehensive analysis of the consequences along each branch and identifies decision nodes that need further analysis.,What are the advantages of decision tree,3829
881,2324,"Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.",What is data science in simple words,2324
882,8726,"While a frequency distribution gives the exact frequency or the number of times a data point occurs, a probability distribution gives the probability of occurrence of the given data point.",What is the difference between probability distribution and relative frequency distribution,8726
883,2782,"A feature vector is a vector containing multiple elements about an object. Putting feature vectors for objects together can make up a feature space. The features may represent, as a whole, one mere pixel or an entire image. The granularity depends on what someone is trying to learn or represent about the object.",What features do vectors have,2782
884,10000,"Based on recent research, we hypothesize that there is a neural network of consciousness in which the paraventricular nucleus formally serves as the control nucleus of arousal, which is closely related to the maintenance of consciousness, and the neurons in the posterior cerebral cortex.",Are neural networks conscious,10000
885,2897,Use systematic sampling when there's low risk of data manipulation. Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation.,What is systematic sampling used for,2897
886,6577,mAP (mean Average Precision) for Object DetectionPrecision & recall.Precision measures how accurate is your predictions.  Recall measures how good you find all the positives.  IoU (Intersection over union)Precision is the proportion of TP = 2/3 = 0.67.Recall is the proportion of TP out of the possible positives = 2/5 = 0.4.,How do you find the accuracy of an object detection,6577
887,2433,"Note: a Markov chain (of any order) is a stochastic recursive sequence of finite order, or equivalently an auto-regressive process of finite order (possibly nonlinear). In contrast, the martingale property does not put constraints on the order of recursion, while imposing a linear projection condition.",Is a martingale a Markov process,2433
888,8842,"Variables that can only take on a finite number of values are called ""discrete variables."" All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree.",Is a discrete variable,8842
889,3517,"There are several situation in which the variable we want to explain can take only two possible values. This is typically the case when we want to model the choice of an individual.  This is why these models are called binary choice models, because they explain a (0/1) dependent variable.",What is a binary choice model,3517
890,7928,"The SVM typically tries to use a ""kernel function"" to project the sample points to high dimension space to make them linearly separable, while the perceptron assumes the sample points are linearly separable.",What is the difference between the perceptron learning algorithm and SVM,7928
891,8227,"Gradient is a vector that is tangent of a function and points in the direction of greatest increase of this function. Gradient is zero at a local maximum or minimum because there is no single direction of increase. In mathematics, gradient is defined as partial derivative for every input variable of function.",What is gradient in data science,8227
892,408,"Assuming a double-blind test is not possible, here are some techniques that can help:Standardize everything: the research protocol, the moderator script, the questions etc.  Have a second researcher monitor the first researcher.  Stay out of the participant's line of sight.  Practice.More items•",How do you remove experimenter bias,408
893,8959,"Cohen's d is an effect size used to indicate the standardised difference between two means. It can be used, for example, to accompany reporting of t-test and ANOVA results. It is also widely used in meta-analysis. Cohen's d is an appropriate effect size for the comparison between two means.",What is Cohen's d in statistics,8959
894,531,Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.,What exactly is Artificial Intelligence,531
895,6655,"The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n).  In between, or when events are infrequent, the Poisson distribution is used.",What is lambda in Poisson distribution,6655
896,1285,"Imbalanced data typically refers to a classification problem where the number of observations per class is not equally distributed; often you'll have a large amount of data/observations for one class (referred to as the majority class), and much fewer observations for one or more other classes (referred to as the",What is the problem with imbalanced data,1285
897,10914,This implies that bias and variance of an estimator are complementary to each other i.e. an estimator with high bias will vary less(have low variance) and an estimator with high variance will have less bias(as it can vary more to fit/explain/estimate the data points).,What is the difference between the bias and variance of an estimator,10914
898,8505,"Chisquare Test, Different Types and its Application using RChi-Square Test.Chi-square test of independence.2 x 2 Contingency Table.Chi-square test of significance.Chi-square Test in R.Chi Square Goodness of Fit (One Sample Test)Chi-square Goodness of Test in R.Fisher's exact test.More items•",What are the two types of chi square tests,8505
899,6651,The generator is a convolutional neural network and the discriminator is a deconvolutional neural network. The goal of the generator is to artificially manufacture outputs that could easily be mistaken for real data. The goal of the discriminator is to identify which outputs it receives have been artificially created.,What is the goal of a generative adversarial network Gan,6651
900,6245,Abstract: The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data.  Fuzzy-kNN computes a fuzzy degree of membership of each instance to the classes of the problem.,What is fuzzy KNN,6245
901,10393,"Statistical Methods for Finding the Best Regression ModelAdjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values.  P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.More items•",How do you know which regression is the best,10393
902,7992,Correlation is the concept of linear relationship between two variables.  It is linear relationship nor any other relationship. Whereas correlation coefficient is a measure that measures linear relationship between two variables.,Is there any difference between correlation and correlation coefficient,7992
903,4674,RECALL is the ratio of the number of relevant records retrieved to the total number of relevant records in the database. It is usually expressed as a percentage. ──────b•d────── Page 2 PRECISION is the ratio of the number of relevant records retrieved to the total number of irrelevant and relevant records retrieved.,What is the relationship between precision and recall,4674
904,3236,"Under simple random sampling, a sample of items is chosen randomly from a population, and each item has an equal probability of being chosen. Meanwhile, systematic sampling involves selecting items from an ordered population using a skip or sampling interval.",What is the difference between simple random sampling and systematic sampling,3236
905,9718,"Sets can be used in calculated fields Sets can be used in calculated fields as if they were a field.  Or you can have the calculation return a specific value, or return another field instead, the main point is that they are not very different than normal dimensions in this respect.",Can we use sets in calculated fields,9718
906,3446,"Concepts in Feature Space Given a set of features for a concept learning problem, we can interpret the feature set as a feature space. Given some data, a feature space is just the set of all possible values for a chosen set of features from that data.",What is concept space in machine learning,3446
907,10232,Scales effectively with data: Deep networks scale much better with more data than classical ML algorithms.  With classical ML algorithms this quick and easy fix doesn't work even nearly as well and more complex methods are often required to improve accuracy.,Can deep learning scale better,10232
908,5370,"In addition, another reason to not initialize everything to zero is so that you get different answers. Some optimization techniques are deterministic, so if you initialize randomly, you'll get different answers each time you run it. This helps you explore the space better and avoid (other) local optima.",Why is zero initialization not a recommended weight initialization technique,5370
909,3221,"Machine learning is more than neural networks and deep learning. It is a field with a legion of smart algorithms that deduce complex patterns and make predictions about the unknown. The robustness of Random forests is contributed to its collection of distinct decision trees, each trying to solve part of the problem.",What have you learned about machine learning,3221
910,3181,"Probability RulesEvery probability is between zero and one. In other words, if A is an event, then 0≤P(A)≤1.The sum of the probabilities of all of the outcomes is one. In other words, if all of the outcomes in the sample space are denoted by Ai, then ∑Ai=1.Impossible events have probability zero.  Certain events have probability one.",What are the probability rules,3181
911,9444,A pooling or subsampling layer often immediately follows a convolution layer in CNN. Its role is to downsample the output of a convolution layer along both the spatial dimensions of height and width.,What is subsampling in CNN,9444
912,167,"Linear Regression is a machine learning algorithm based on supervised learning. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x).  So, this regression technique finds out a linear relationship between x (input) and y(output).",How does regression algorithm work,167
913,3918,(retrogress) Opposite of to develop gradually. retrogress. diminish. regress.,What is the opposite of evolve,3918
914,8922,"YOUR preferred learning style is the way in which YOU learn best. Three learning styles that are often identified in students are the Auditory Learning Style, the Visual Learning Style, and theTactile/Kinesthetic Learning Style. Read about each of these learning styles to identify YOUR preferred learning style.",What is your preferred way of learning,8922
915,1816,Not usually. SGD tends to perform better than using line search.,Is line search used commonly with SGD while learning the parameters for a deep neural networks,1816
916,1996,"If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points. df=n−2=10−2=8.",Is it possible to determine the statistical significance of a correlation coefficient,1996
917,7670,Correlation coefficients are indicators of the strength of the relationship between two different variables. A correlation coefficient that is greater than zero indicates a positive relationship between two variables. A value that is less than zero signifies a negative relationship between two variables.,What do different correlation coefficients mean,7670
918,4901,"Classification is a supervised machine learning approach, in which the algorithm learns from the data input provided to it — and then uses this learning to classify new observations.  The name (""Naive"") derives from the fact that the algorithm assumes that attributes are conditionally independent.",What is classification techniques in machine learning,4901
919,600,"Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer)",How does neural network reduce loss,600
920,2039,"This significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set. Interchanging the training and test sets also adds to the effectiveness of this method.",What statistics does cross validation reduce,2039
921,5259,"High Pass RL Filter An inductor, like a capacitor, is a reactive device.  And this is why this circuit is a high-pass filter circuit. Low frequency signals, however, will go through the inductor, because inductors offer very low resistance to low-frequency, or Dc, signals.",Is inductor high pass filter,5259
922,7410,Clustering and Association are two types of Unsupervised learning.  Important clustering types are: 1)Hierarchical clustering 2) K-means clustering 3) K-NN 4) Principal Component Analysis 5) Singular Value Decomposition 6) Independent Component Analysis.,Which of the following is an unsupervised learning algorithm,7410
923,8835,"Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with the maximum correlation. The traditional CCA can only be used to calculate the linear correlation between two views.",What is CCA in machine learning,8835
924,1037,"In mathematics, a tensor is an algebraic object that describes a (multilinear) relationship between sets of algebraic objects related to a vector space. Objects that tensors may map between include vectors and scalars, and even other tensors.",How do you define a tensor,1037
925,2621,"With two-way ANOVA, you have one continuous dependent variable and two categorical grouping variables for the independent variables. MANOVA models several dependent variables simultaneously and you can include a variety of independent variables.",What is the difference between a 2 way ANOVA and a MANOVA,2621
926,8306,"by Tim Bock. Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.",What is hierarchical clustering algorithm,8306
927,8141,"In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points.  It is often required to interpolate, i.e., estimate the value of that function for an intermediate value of the independent variable.",What is interpolation algorithm,8141
928,7502,"To find “q” or the studentized range statistic, refer to your table on page A-32 of your text. On the table 'k' or the number of groups is found along the top, and degrees of freedom within is down the side.",How do you find q in Tukey's HSD,7502
929,7361,"Such algorithms are called greedy because while the optimal solution to each smaller instance will provide an immediate output, the algorithm doesn't consider the larger problem as a whole.  Greedy algorithms work by recursively constructing a set of objects from the smallest possible constituent parts.",Why is it called greedy algorithm,7361
930,7877,"Multi-view data is common in real-world datasets, where different views describe distinct perspec- tives.  Multi-view data is prevalent in many real-world applications. For instance, the same news can be obtained from various language sources; an image can be described by different low level visual features.",What is multi view data,7877
931,7027,"Perceptron networks have several limitations. First, the output values of a perceptron can take on only one of two values (0 or 1) due to the hard-limit transfer function. Second, perceptrons can only classify linearly separable sets of vectors.",What are the limitations of Perceptron,7027
932,6916,"A vector space is a space of vectors, ie. each element is a vector. A vector field is, at its core, a function between some space and some vector space, so every point in our base space has a vector assigned to it. A good example would be wind direction maps you see on weather reports.",What is the difference between a vector field and a vector space,6916
933,3035,"Structural equation models are often used to assess unobservable 'latent' constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables.",When would you use a structural equation model,3035
934,4797,"AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.",What is a good area under ROC curve,4797
935,6888,Grid search is an approach to hyperparameter tuning that will methodically build and evaluate a model for each combination of algorithm parameters specified in a grid.,What is a grid search and why do we use it in machine learning,6888
936,2748,Classification and regression trees are machine-learning methods for constructing. prediction models from data. The models are obtained by recursively partitioning. the data space and fitting a simple prediction model within each partition.,What is classification and regression tree,2748
937,8792,"A correlation close to -1 or 1 tells us that there is a strong relationship between the variables. It is useful to know this. Strictly speaking, it applies to a linear relationship, but the correlation can be high even for an obviously curvilinear relationship.",What is the advantage of a correlation coefficient,8792
938,4850,Step 1: Divide your confidence level by 2: .95/2 = 0.475. Step 2: Look up the value you calculated in Step 1 in the z-table and find the corresponding z-value. The z-value that has an area of .475 is 1.96. Step 3: Divide the number of events by the number of trials to get the “P-hat” value: 24/160 = 0.15.,How do you find confidence intervals,4850
939,5968,Computer Vision. Image processing is mainly focused on processing the raw input images to enhance them or preparing them to do other tasks. Computer vision is focused on extracting information from the input images or videos to have a proper understanding of them to predict the visual input like human brain.,What is computer vision and image processing,5968
940,3791,"Variance plays a major role in interpreting data in statistics. The most common application of variance is in polls. For opinion polls, the data gathering agencies cannot invest in collecting data from the entire population.",How is variance used in real life,3791
941,8401,Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters.,What is maximum likelihood estimation in machine learning,8401
942,4467,"In mathematics, specifically in functional analysis, each bounded linear operator on a complex Hilbert space has a corresponding Hermitian adjoint (or adjoint operator). Adjoints of operators generalize conjugate transposes of square matrices to (possibly) infinite-dimensional situations.",What is the adjoint of a linear operator,4467
943,3371,Adding Noise into Neural Network Neural networks are capable of learning output functions that can change wildly with small changes in input. Adding noise to inputs randomly is like telling the network to not change the output in a ball around your exact input.,What is noise in neural network,3371
944,1575,"They have too few levels of structure: Neurons, Layers, and Whole Nets. We need to group neurons in each layer in 'capsules' that do a lot of internal computation and then output a compact result.”",What is wrong with convolutional neural nets,1575
945,8925,Qualities of a Good Sampling Frame Include all individuals in the target population. Exclude all individuals not in the target population. Includes accurate information that can be used to contact selected individuals.,What is a good sampling frame,8925
946,2382,The resulting image after applying Canny operator (b). The primary advantages of the Sobel operator lie in its simplicity. The Sobel method provides a approximation to the gradient magnitude. Another advantage of the Sobel operator is it can detect edges and their orientations.,What is the advantage of Sobel operator over Prewitt operator,2382
947,6847,We present a freely available open-source toolkit for training recurrent neural network based language models. It can be easily used to improve existing speech recognition and machine translation systems.,Is there a recurrent neural networks toolkit,6847
948,7870,"Definition. The class intervals are the subsets into which the data is grouped. The width of the class intervals will be a compromise between having intervals short enough so that not all of the observations fall in the same interval, but long enough so that you do not end up with only one observation per interval.",What is class interval in statistics with example,7870
949,10402,"A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly.  If you accept most classes of problems can be reduced to functions, this statement implies a neural network can, in theory, solve any problem.",Can neural networks solve any problem,10402
950,6297,"The dissimilarity matrix, using the euclidean metric, can be calculated with the command: daisy(agriculture, metric = ""euclidean""). The result the of calculation will be displayed directly in the screen, and if you wanna reuse it you can simply assign it to an object: x <- daisy(agriculture, metric = ""euclidean"").",How do you find the dissimilarity of a matrix,6297
951,5656,Face validity: Does the content of the test appear to be suitable to its aims? Criterion validity: Do the results correspond to a different test of the same thing?,What is the difference between content and criterion validity,5656
952,6238,"In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path.",What is admissible heuristic in AI,6238
953,10240,"In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.  Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex.",What is meant by convolution neural network,10240
954,8026,"Average (or mean) filtering is a method of 'smoothing' images by reducing the amount of intensity variation between neighbouring pixels. The average filter works by moving through the image pixel by pixel, replacing each value with the average value of neighbouring pixels, including itself.",What is average filtering,8026
955,4586,"The Elbow Method is more of a decision rule, while the Silhouette is a metric used for validation while clustering. Thus, it can be used in combination with the Elbow Method. Therefore, the Elbow Method and the Silhouette Method are not alternatives to each other for finding the optimal K.",Which method is not used for finding the best K in K means technique,4586
956,889,"Normalization: Similarly, the goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values.  So we normalize the data to bring all the variables to the same range.",Why do we normalize data,889
957,9935,"Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.",Is naive Bayes generative or discriminative,9935
958,7055,"In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions.",What is the multivariate Gaussian distribution,7055
959,2086,Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks.,Why does normalization work in batch,2086
960,6687,"Summary: “OLS” stands for “ordinary least squares” while “MLE” stands for “maximum likelihood estimation.”  Maximum likelihood estimation, or MLE, is a method used in estimating the parameters of a statistical model and for fitting a statistical model to data.",What is the difference between OLS and Maximum Likelihood,6687
961,3575,"Ensemble learning helps improve machine learning results by combining several models.  Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).",How does ensemble method work,3575
962,4052,"Data Preprocessing is a technique that is used to convert the raw data into a clean data set.  In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.",What is data preprocessing in ML,4052
963,3968,In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables.,What is the difference between linear regression and multiple linear regression,3968
964,6475,"The principal advantage of linear regression is its simplicity, interpretability, scientific acceptance, and widespread availability. Linear regression is the first method to use for many problems. Analysts can use linear regression together with techniques such as variable recoding, transformation, or segmentation.",What is the main advantage of using linear regression,6475
965,10357,"Consider the normal distribution N(100, 10). To find the percentage of data below 105.3, that is P(x < 105.3), standartize first: P(x < 105.3) = P ( z < 105.3 − 100 10 ) = P(z < 0.53). Then find the proportion corresponding to 0.53 in Table A: look for the intersection of the row labeled 0.5 and the column labeled .",How do you find the standard normal distribution percentage,10357
966,1441,"ReLU stands for rectified linear unit, and is a type of activation function. Mathematically, it is defined as y = max(0, x). Visually, it looks like the following: ReLU is the most commonly used activation function in neural networks, especially in CNNs.",What is ReLU in machine learning,1441
967,1700,"You can use regression equations to make predictions. Regression equations are a crucial part of the statistical output after you fit a model.  However, you can also enter values for the independent variables into the equation to predict the mean value of the dependent variable.",Is it appropriate to use the linear regression equation to make predictions,1700
968,8699,Explanation: Weight adjustment is proportional to negative gradient of error with respect to weight. 10.,Does backpropagation learning is based on gradient descent along error surface,8699
969,3612,"Binomial is defined as a math term meaning two expressions connected by a plus or minus sign. An example of a binomial is x – y.  An example of a binomial is Canis familiaris, the scientific name for dog.",What is binomial example,3612
970,799,Data Collection & Analysis Tools Related TopicsBox & Whisker Plot.Check Sheet.Control Chart.Design of Experiments (DOE)Histogram.Scatter Diagram.Stratification.Survey.,What are the tools for analysis,799
971,1165,"In data science, association rules are used to find correlations and co-occurrences between data sets. They are ideally used to explain patterns in data from seemingly independent information repositories, such as relational databases and transactional databases.",What is the applicability of association rules,1165
972,10835,"Linear Regression, intuitively is a regression algorithm with a Linear approach. We try to predict a continuous value of a given data point by generalizing on the data that we have in hand. The linear part indicates that we are using a linear approach in generalizing over the data.",What is the intuition behind linear regression,10835
973,8492,"Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced and there's class disparity, then other methods like ROC/AUC perform better in evaluating the model performance.",What are the metrics chosen to evaluate model performance,8492
974,7758,The two are different. Stoichiometry looks at balancing equations whereas dimensional analysis is looking at the units particular equations take and allowing you to make a determination of final units (and possibly the correctness of your derivation of units for any equations).,Is dimensional analysis the same thing as stoichiometry,7758
975,2798,"Examples of sampling bias include self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding subjects who have recently moved into or out of the study area.",What is selection bias example,2798
976,10566,"Gradient Boosting Machines vs. XGBoost.  While regular gradient boosting uses the loss function of our base model (e.g. decision tree) as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation.",What's the difference between gradient boosting and XGBoost,10566
977,8493,Introduction to K-Means ClusteringStep 1: Choose the number of clusters k.  Step 2: Select k random points from the data as centroids.  Step 3: Assign all the points to the closest cluster centroid.  Step 4: Recompute the centroids of newly formed clusters.  Step 5: Repeat steps 3 and 4.,How do you use K means clustering,8493
978,7569,"In statistics, a confounder (also confounding variable, confounding factor, or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.",What is meant by confounding in statistics,7569
979,735,"Initializers define the way to set the initial random weights of Keras layers. The keyword arguments used for passing initializers to layers depends on the layer. Usually, it is simply kernel_initializer and bias_initializer : from tensorflow.keras import layers from tensorflow.keras import initializers layer = layers.",What is kernel initializer in keras,735
980,8575,The 1×1 filter can be used to create a linear projection of a stack of feature maps. The projection created by a 1×1 can act like channel-wise pooling and be used for dimensionality reduction. The projection created by a 1×1 can also be used directly or be used to increase the number of feature maps in a model.,How are 1x1 convolutions used for dimensionality reduction,8575
981,4332,"The null hypothesis (H0) for a one tailed test is that the mean is greater (or less) than or equal to µ, and the alternative hypothesis is that the mean is < (or >, respectively) µ.",What is the null hypothesis for a one tailed test,4332
982,1798,"It's greedy because you always mark the closest vertex. It's dynamic because distances are updated using previously calculated values. I would say it's definitely closer to dynamic programming than to a greedy algorithm. To find the shortest distance from A to B, it does not decide which way to go step by step.",Why is Dijkstra A greedy algorithm,1798
983,2564,"all provides a way to leverage binary classification. -all solution consists of N separate binary classifiers—one binary classifier for each possible outcome.  During training, the model runs through a sequence of binary classifiers, training each to answer a separate classification question.",What is one vs all classification,2564
984,3058,"By reversing the words in the source sentence, the average distance between corresponding words in the source and target language is unchanged. However, the first few words in the source language are now very close to the first few words in the target language, so the problem's minimal time lag is greatly reduced.",In the paper Sequence to Sequence Learning with Neural Networks why does reversing the source sentence allow better performance on longer sentences,3058
985,6024,"The main difference is that ABM typically implement low numbers of highly complex agents, and the main feature they consider are their individual capabilities to face the task. On the opposite, MAS consider (very) large numbers of simpler agents, focusing on the emergence of new phenomena from social interactions.",What is the difference between agent based simulation ABS and multi agent system MAS ),6024
986,4541,"A box and whisker plot—also called a box plot—displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum.",What values are in a box plot,4541
987,5651,Establish face validity.Conduct a pilot test.Enter the pilot test in a spreadsheet.Use principal component analysis (PCA)Check the internal consistency of questions loading onto the same factors.Revise the questionnaire based on information from your PCA and CA.,How do you check for the validity and reliability of a questionnaire,5651
988,4749,"""A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.""  It is also called a Bayes network, belief network, decision network, or Bayesian model.",What is Bayesian network in AI,4749
989,2804,"They are basically equivalent: the linear time invariant systems refers to an analog system and shift-invariant system refers to a discrete-time system.  The shift-invariant is the same as time invariant: if we delay the input, the output that we get is the original input to the signal that wasn't delayed.",What is difference between Linear time invariant system and Linear shift invariant system 1,2804
990,6090,"Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in the development and designing of different software.",Which program is used for artificial intelligence,6090
991,461,A random variable is a variable whose value is a numerical outcome of a random phenomenon. A discrete random variable X has a countable number of possible values. Example: Let X represent the sum of two dice.  A continuous random variable X takes all values in a given interval of numbers.,What is the difference between a random variable and a discrete random variable,461
992,1135,"Linear Shift-Invariant systems, called LSI systems for short, form a very important class of practical systems, and hence are of interest to us. They are also referred to as Linear Time-Invariant systems, in case the independent variable for the input and output signals is time.",What is linear shift invariant system,1135
993,10238,Now we'll check out the proven way to improve the accuracy of a model:Add more data. Having more data is always a good idea.  Treat missing and Outlier values.  Feature Engineering.  Feature Selection.  Multiple algorithms.  Algorithm Tuning.  Ensemble methods.,How can the accuracy of a linear regression model be improved,10238
994,5127,Inductive probability refers to the likelihood that an inductive argument with true premises will give a true conclusion.  An argument with low inductive probability is less likely to have a true conclusion even if its premises are true.,What is a probability argument,5127
995,6869,The Softmax regression is a form of logistic regression that normalizes an input value into a vector of values that follows a probability distribution whose total sums up to 1.,How does Softmax regression work,6869
996,7583,"Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.",Why is autocorrelation a problem for times series analysis,7583
997,4026,"First, after looking around on the web, it seems that there is no way to compute a (discrete) Fourier transform through a neural network. You can hack it by hard-coding the thing to include the Fourier constants for the transform and then get a decent result.",Can neural networks learn Fourier Transform,4026
998,7743,"Given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens , perhaps at the same time throwing away certain characters, such as punctuation.",What does tokenization mean in NLP,7743
999,9754,"A sample survey can be broadly defined as an exercise that involves collecting standardised data from a sample of study units (e.g., persons, households, businesses) designed to represent a larger population of units, in order to make quantitative inferences about the population.",What is sample survey method,9754
1000,27,"(e.g. if P=1/256, that's 8 bits.) Entropy is just the average of that information bit length, over all the outcomes. The purpose of log(pi) appearing in Shannon's Entropy is that log(pi) is the only function satisfying the basic set of properties that the entropy function, H(p1,…,pN), is held to embody.",Why log is used in entropy,27
1001,7469,Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model's performance on the unseen data as well.,What are regularization techniques,7469
1002,8924,"In machine learning, scoring is the process of applying an algorithmic model built from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem.  The second stage is scoring, in which you apply the trained model to a new dataset.",What is model scoring in machine learning,8924
1003,2165,"The Matrix represents a system of control that operates completely in the mind. As a complex, machine-driven program, it appropriates any personal, political, or ideological leanings and renders them wholly false. It allows illusions but no action.",What is the Matrix and what does it represent,2165
1004,3500,"Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).",What are statistical methods used for analysis,3500
1005,8986,"The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI wasn't formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term ""artificial intelligence"" was coined.",What is the history of artificial intelligence,8986
1006,1582,"Causation is the relationship between cause and effect. So, when a cause results in an effect, that's a causation.  When we say that correlation does not imply cause, we mean that just because you can see a connection or a mutual relationship between two variables, it doesn't necessarily mean that one causes the other.",Does correlation imply causation Why or why not,1582
1007,594,"more  A symbol for a value we don't know yet. It is usually a letter like x or y. Example: in x + 2 = 6, x is the variable.",What is variable example,594
1008,5948,"1. If having conditional independence will highly negative affect classification, you'll want to choose K-NN over Naive Bayes. Naive Bayes can suffer from the zero probability problem; when a particular attribute's conditional probability equals zero, Naive Bayes will completely fail to produce a valid prediction.",Classification machine learning When should I use a K NN classifier over a Naive Bayes classifier,5948
1009,8991,"A time series is a dataset whose unit of analysis is a time period, rather than a person. Regression is an analytic tool that attempts to predict one variable, y as a function of one or more x variables. It can be used to analyze both time-series and static data.",What is the difference between regression and time series,8991
1010,7931,Inverted Dropout is how Dropout is implemented in practice in the various deep learning frameworks because it helps to define the model once and just change a parameter (the keep/drop probability) to run train and test on the same model.,Why is the dropout inverted,7931
1011,3949,"If exploding gradients are still occurring, you can check for and limit the size of gradients during the training of your network. This is called gradient clipping. Dealing with the exploding gradients has a simple but very effective solution: clipping gradients if their norm exceeds a given threshold.",What technique is followed to deal with the problem of exploding gradients in recurrent neural net works RNN ),3949
1012,7960,Statistics is generally considered a prerequisite to the field of applied machine learning. We need statistics to help transform observations into information and to answer questions about samples of observations.,How is statistics related to machine learning,7960
1013,4184,"Thus, the SMC counts both mutual presences (when an attribute is present in both sets) and mutual absence (when an attribute is absent in both sets) as matches and compares it to the total number of attributes in the universe, whereas the Jaccard index only counts mutual presence as matches and compares it to the",What is the main difference between simple matching coefficient SMC similarity and Jaccard similarity,4184
1014,10041,"If you use import numpy , all sub-modules and functions in the numpy module can only be accesses in the numpy.  If you use from numpy import * , all functions will be loaded into the local namespace. For example array([1,2,3]) can then be used.",What is the difference between import numpy and from numpy import *,10041
1015,8278,"For independent random variables X and Y, the variance of their sum or difference is the sum of their variances: Variances are added for both the sum and difference of two independent random variables because the variation in each variable contributes to the variation in each case.",How do you find the variance of an independent variable,8278
1016,4760,"The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis.",What does the T score tell you,4760
1017,3107,Loss curves are a standard actuarial technique for helping insurance companies assess the amount of reserve capital they need to keep on hand to cover claims from a line of business. Claims made and reported for a given accounting period are tracked seperately over time.,What is a loss curve,3107
1018,7626,0:007:21Suggested clip · 102 secondsBayesian posterior sampling - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you sample a posterior distribution,7626
1019,1946,"This is referred to as the joint probability of X = x and Y = y. If X and Y are discrete random variables, the function given by f (x, y) = P(X = x, Y = y) for each pair of values (x, y) within the range of X is called the joint probability distribution of X and Y .",What is the joint distribution of X and Y,1946
1020,1455,"In addition, scales can be constructed from categorical variables. This is covered in a later section. The Count property returns the number of levels in the scale. The IsOrdered property indicates whether the scale is ordered or unordered.",Can Scaling be applied to categorical variables,1455
1021,7092,"KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression).",How does KNN classification work,7092
1022,418,"A mutually exclusive pair of events are complements to each other. For example: If the desired outcome is heads on a flipped coin, the complement is tails. The Complement Rule states that the sum of the probabilities of an event and its complement must equal 1, or for the event A, P(A) + P(A') = 1.",How is the rule of complement used to calculate probability,418
1023,258,"(8) The moment generating function corresponding to the normal probability density function N(x;µ, σ2) is the function Mx(t) = exp{µt + σ2t2/2}.",What is the moment generating function of normal distribution,258
1024,9012,Step 1 — Deciding on the network topology (not really considered optimization but is obviously very important)  Step 2 — Adjusting the learning rate.  Step 3 — Choosing an optimizer and a loss function.  Step 4 — Deciding on the batch size and number of epochs.  Step 5 — Random restarts.,How do I tune Hyperparameters in neural network,9012
1025,2877,"To work out the probability that a discrete random variable X takes a particular value x, we need to identify the event (the set of possible outcomes) that corresponds to ""X=x"". pX(x)=Pr(X=x). In general, the probability function pX(x) may be specified in a variety of ways.",What is the function of probability,2877
1026,7711,"12 Common Logical Fallacies and How to Debunk Them12 Common Logical Fallacies and How to Debunk Them.  Ad Hominem.  Appeal to Authority.  Bandwagon Argument, or ad populum.  The Strawman.  Circular Reasoning.  The Genetic Fallacy.  Anecdotal Evidence.More items•",What are the 12 fallacies,7711
1027,1457,Five main Component of Natural Language processing are:Morphological and Lexical Analysis.Syntactic Analysis.Semantic Analysis.Discourse Integration.Pragmatic Analysis.,What are the components of NLP,1457
1028,8369,x̄ = ( Σ xi ) / nAdd up the sample items.Divide sum by the number of samples.The result is the mean.Use the mean to find the variance.Use the variance to find the standard deviation.,How do you find the mean of a random sample,8369
1029,1065,"Generalized Linear Models let you express the relation between covariates X and response y in a linear, additive manner.",What is the practical purpose of generalized linear models,1065
1030,4736,"We can use the regression line to predict values of Y given values of X. For any given value of X, we go straight up to the line, and then move horizontally to the left to find the value of Y. The predicted value of Y is called the predicted value of Y, and is denoted Y'.",How do you find the predicted value in a regression equation,4736
1031,3346,"Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target.",Which method gives the best fit for logistic regression model,3346
1032,5082,"2 Answers. Boosting is based on weak learners (high bias, low variance).  Boosting reduces error mainly by reducing bias (and also to some extent variance, by aggregating the output from many models). On the other hand, Random Forest uses as you said fully grown decision trees (low bias, high variance).",What is the difference in bias and variance in 1 Random Forest 2 gradient boosting Why is there this difference,5082
1033,8743,"Some applications of unsupervised machine learning techniques include: Clustering allows you to automatically split the dataset into groups according to similarity. Often, however, cluster analysis overestimates the similarity between groups and doesn't treat data points as individuals.",What are the application of unsupervised learning,8743
1034,2776,"Because our sample size is greater than 30, the Central Limit Theorem tells us that the sampling distribution will approximate a normal distribution.  Because we know the population standard deviation and the sample size is large, we'll use the normal distribution to find probability.",Why is normal distribution used in sampling distribution,2776
1035,7497,"We find the robust standard deviation estimate by multiplying the MAD by a factor that happens to have a value close to 1.5. This gives us a robust value ('sigma- hat') of B . . If we use this method on data without outliers, it provides estimates that are close to x and s, so no harm is done.",How is robust standard deviation calculated,7497
1036,173,"A statistical test provides a mechanism for making quantitative decisions about a process or processes. The intent is to determine whether there is enough evidence to ""reject"" a conjecture or hypothesis about the process.  A classic use of a statistical test occurs in process control studies.",What are statistical tests in research,173
1037,7667,"13. What is the difference between unimodal, bimodal, and multimodal data? Unimodal data has a distribution that is single-peaked (one mode). Bimodal data has two peaks (2 modes) and multimodal data refer to distributions with more than two clear peaks.",What is the difference between unimodal and multimodal,7667
1038,10552,"In project management terms, an s-curve is a mathematical graph that depicts relevant cumulative data for a project—such as cost or man-hours—plotted against time.  An s-curve in project management is typically used to track the progress of a project.",What does an S curve represent,10552
1039,3884,"K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970's as a non-parametric technique.",What is K nearest Knn data mining algorithm,3884
1040,9173,"Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce.  First, you will learn how to install Spark with all new features from the latest Spark 2.0 release.",Why is Spark efficient for large scale machine learning,9173
1041,1935,"Hello every one, We know that Pearson linear correlation coefficient gives the strength of linear relationship, while Spearman rank correlation coefficient gives the strength of monotonic relationship between two variables.",Is rank correlation coefficient different from Pearson correlation coefficient explain with reason,1935
1042,4782,"It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm.",Does AlphaGo use reinforcement learning,4782
1043,9484,"Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.",What is data mining classification,9484
1044,1415,"Matrix Inventory allows you to add and manage product lists that consist of similar items that are available in a variety of attributes, such as size or color.  Each product is defined by a combination of attributes is a unique product with its own price, inventory and/or recipe.",What is Matrix inventory,1415
1045,8425,"Since a Naive Bayes text classifier is based on the Bayes's Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.",Why is naive Bayes used for text classification,8425
1046,10891,"Standard interpretation of the ordered logit coefficient is that for a one unit increase in the predictor, the response variable level is expected to change by its respective regression coefficient in the ordered log-odds scale while the other variables in the model are held constant.",How do you interpret ordered logit coefficients,10891
1047,3425,The formula is:P(A|B) = P(A) P(B|A)P(B)P(Man|Pink) = P(Man) P(Pink|Man)P(Pink)P(Man|Pink) = 0.4 × 0.1250.25 = 0.2.Both ways get the same result of ss+t+u+v.P(A|B) = P(A) P(B|A)P(B)P(Allergy|Yes) = P(Allergy) P(Yes|Allergy)P(Yes)P(Allergy|Yes) = 1% × 80%10.7% = 7.48%More items,How do you calculate Bayesian probability,3425
1048,468,"Difference between Z score vs T score. Z score is a conversion of raw data to a standard score, when the conversion is based on the population mean and population standard deviation.  T score is a conversion of raw data to the standard score when the conversion is based on the sample mean and sample standard deviation.",What is the difference between T score and Z score,468
1049,8005,"He doesn't explicitly betray Kaneki, but it seems like it because someone who seemed like such a nice guy, giving advice to Kaneki and helping retrieve him from Aogiri, ended up being a sadistic and manipulative person.",Did Uta betray kaneki,8005
1050,10396,"Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set.",What is a variance in statistics,10396
1051,7706,"Put simply: random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random forest has nearly the same hyperparameters as a decision tree or a bagging classifier.  Random forest adds additional randomness to the model, while growing the trees.",How does a Random Forest model work,7706
1052,1146,"We can say that, when we move from RNN to LSTM, we are introducing more & more controlling knobs, which control the flow and mixing of Inputs as per trained Weights. And thus, bringing in more flexibility in controlling the outputs. So, LSTM gives us the most Control-ability and thus, Better Results.",Why is Lstm better than RNN,1146
1053,636,"When examining the distribution of a quantitative variable, one should describe the overall pattern of the data (shape, center, spread), and any deviations from the pattern (outliers).",How do you describe the distribution,636
1054,10432,"To find the interquartile range (IQR), ​first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.",How do you determine the interquartile range,10432
1055,2155,"Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space.",What is a clustering algorithm,2155
1056,9829,"A Convolutional neural network (CNN) is a neural network that has one or more convolutional layers and are used mainly for image processing, classification, segmentation and also for other auto correlated data. A convolution is essentially sliding a filter over the input.",What are convolutional neural networks used for,9829
1057,7382,Entropy is the measure of disorder in a thermodynamic system.Difference Between Enthalpy and EntropyEnthalpy is a kind of energyEntropy is a propertyIt is the sum of internal energy and flows energyIt is the measurement of the randomness of moleculesIt is denoted by symbol HIt is denoted by symbol S5 more rows,What is the difference between entropy and entropy,7382
1058,2195,The logistic function is the inverse of the natural logit function and so can be used to convert the logarithm of odds into a probability. In mathematical notation the logistic function is sometimes written as expit in the same form as logit.,What is the logistic function used for,2195
1059,1642,If you want to ingest DynamoDB data into Redshift you have a few options.The Redshift Copy command.Build a Data Pipeline that copies the data using an EMR job to S3.Export the DynamoDB data to a file using the AWS CLI and load the flat file into Redshift.More items,How do I transfer data from DynamoDB to redshift,1642
1060,3887,"An endogenous variable is a variable in a statistical model that's changed or determined by its relationship with other variables within the model.  Therefore, its values may be determined by other variables. Endogenous variables are the opposite of exogenous variables, which are independent variables or outside forces.",What is an endogenous variable in regression,3887
1061,1481,"A continuous random variable takes a range of values, which may be finite or infinite in extent. Here are a few examples of ranges: [0, 1], [0, ∞), (−∞, ∞), [a, b]. The function f(x) is called the probability density function (pdf).",What is range where X is a continuous random variable,1481
1062,7191,"A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects.  Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA.",What is a mixed model in statistics,7191
1063,10061,"ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network uses memistors.  It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.",What is Adaline in neural network,10061
1064,10231,"LDA assumes documents are produced from a mixture of topics. Those topics then generate words based on their probability distribution. Given a dataset of documents, LDA backtracks and tries to figure out what topics would create those documents in the first place. LDA is a matrix factorization technique.",How does LDA modeling work,10231
1065,4274,"Ridge regression is a term used to refer to a linear regression model whose coefficients are not estimated by ordinary least squares (OLS), but by an estimator, called ridge estimator, that is biased but has lower variance than the OLS estimator.",What is the difference between OLS and ridge regression,4274
1066,4011,"The classic approach to the multiple comparison problem is to control the familywise error rate. Instead of setting the critical P level for significance, or alpha, to 0.05, you use a lower critical value.",How do you control multiple comparisons,4011
1067,358,"Conditional probability is defined as the likelihood of an event or outcome occurring, based on the occurrence of a previous event or outcome. Conditional probability is calculated by multiplying the probability of the preceding event by the updated probability of the succeeding, or conditional, event.",What do you mean by conditional probability,358
1068,4271,They are often confused with each other. The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.,What is the difference between Knn and K means clustering,4271
1069,4225,"Support Vector Machine algorithms are supervised learning models that analyse data used for classification and regression analysis. They essentially filter data into categories, which is achieved by providing a set of training examples, each set marked as belonging to one or the other of the two categories.",What is an algorithm in machine learning,4225
1070,7537,"Boosting differs somewhat from bagging as it does not involve bootstrap sampling. Instead models are generated sequentially and iteratively, meaning that it is necessary to have information about model before iteration is produced. Boosting was motivated by Kearns and Valiant (1989).",Does boosting use bootstrapping,7537
1071,1664,"Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.",What is transfer learning in ML,1664
1072,5900,"Below are the methods to convert a categorical (string) input to numerical nature:Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables).  Convert numeric bins to number: Let's say, bins of a continuous variable are available in the data set (shown below).",How do you deal with categorical variables in machine learning,5900
1073,7218,"There are two types of sampling methods: Probability sampling involves random selection, allowing you to make statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data.",What are the types of probability and non probability sampling,7218
1074,4888,The purpose of statistical inference is to estimate this sample to sample variation or uncertainty.,What is the purpose of statistical inference,4888
1075,10365,"The first component is the definition: Two variables are independent when the distribution of one does not depend on the the other.  If the probabilities of one variable remains fixed, regardless of whether we condition on another variable, then the two variables are independent.",What does it mean if two variables are independent,10365
1076,9120,Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks). The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers.  NLP is a part of AI which overlaps with ML & DL.,Does NLP come under deep learning,9120
1077,3110,"CRF is a discriminant model. MEMM is not a generative model, but a model with finite states based on state classification. HMM and MEMM are a directed graph, while CRF is an undirected graph. HMM directly models the transition probability and the phenotype probability, and calculates the probability of co-occurrence.",What is the major difference between CRF Conditional Random Field and HMM hidden Markov model,3110
1078,259,"5 Answers. The Fourier series is used to represent a periodic function by a discrete sum of complex exponentials, while the Fourier transform is then used to represent a general, nonperiodic function by a continuous superposition or integral of complex exponentials.",What is the relationship between the Fourier transform and Fourier series representation of a continuous function,259
1079,244,"A correlation coefficient that is greater than zero indicates a positive relationship between two variables. A value that is less than zero signifies a negative relationship between two variables. Finally, a value of zero indicates no relationship between the two variables that are being compared.",What should the correlation of variable when compared with itself *,244
1080,273,"In marketing terms, a multi-armed bandit solution is a 'smarter' or more complex version of A/B testing that uses machine learning algorithms to dynamically allocate traffic to variations that are performing well, while allocating less traffic to variations that are underperforming.",What is a bandit algorithm,273
1081,3606,"Spatiotemporal data mining refers to the process of discovering patterns and knowledge from spatiotemporal data.  Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers.",What is spatio temporal data mining,3606
1082,1950,ASUS EZ Flash 3 allows you to download and update to the latest BIOS through the Internet without having to use a bootable disk or an OS-based utility.,What is EZ flash,1950
1083,10545,Hickam's dictum,What's the opposite of Occam's razor,10545
1084,966,"Random utility theory is based on the hypothesis that every individual is a rational decision-maker, maximizing utility relative to his or her choices. Specifically, the theory is based on the following assumptions.",What is random utility model,966
1085,5474,"In probability, an outcome is in event ""A and B"" only when the outcome is in both event A and event B. (Intersection) In a Venn Diagram, an element is in the intersection of ""A and B"" only when the element is in BOTH sets. Rule (for AND):",What is the and rule in probability,5474
1086,2949,"Algorithms consist of instructions that are carried out (performed) one after another. Sequencing is the specific order in which instructions are performed in an algorithm. For example, a very simple algorithm for brushing teeth might consist of these steps: put toothpaste on toothbrush.",What is the difference between sequence and algorithm,2949
1087,873,"XFL teams will have two timeouts per half, one fewer than in the NFL. Halftime is 10 minutes, two minutes less than the NFL. Another attempt to shorten the game is not allowing coaches to challenge an official's ruling. All plays are subject to review by the replay official.",What rules are different in the XFL,873
1088,9870,"2 Key Challenges of Streaming Data and How to Solve ThemStreaming Data is Very Complex. Streaming data is particularly challenging to handle because it is continuously generated by an array of sources and devices and is delivered in a wide variety of formats.  Business Wants Data, But IT Can't Keep Up.",What are the challenges of data stream processing,9870
1089,9271,"Linear regression is used to find the best fitting line between all the points of your dataset (by computing the minimum of a given distance), it does not, in itself, reduce the dimensionality of your data.",Is dimensionality reduction applicable in simple linear regression,9271
1090,1842,"A paired t-test is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. For example, in the Dixon and Massey data set we have cholesterol levels in 1952 and cholesterol levels in 1962 for each subject.",Why would you use a paired t test,1842
1091,8105,General linear modeling in SPSS for Windows The general linear model (GLM) is a flexible statistical model that incorporates normally distributed dependent variables and categorical or continuous independent variables.,What is general linear model in SPSS,8105
1092,3756,"The Basics of a One-Tailed Test Hypothesis testing is run to determine whether a claim is true or not, given a population parameter. A test that is conducted to show whether the mean of the sample is significantly greater than and significantly less than the mean of a population is considered a two-tailed test.",What do you mean by one tailed test and two tailed test,3756
1093,10294,"An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time.  Depending upon the process to develop the network there are three main models of machine learning: Unsupervised learning. Supervised learning.",What is learning in neural networks,10294
1094,9598,"A moving average term in a time series model is a past error (multiplied by a coefficient). Let w t ∼ i i d N ( 0 , σ w 2 ) , meaning that the wt are identically, independently distributed, each with a normal distribution having mean 0 and the same variance.",What is moving average model in time series,9598
1095,9341,"A factorial distribution happens when a set of variables are independent events. In other words, the variables don't interact at all; Given two events x and y, the probability of x doesn't change when you factor in y.",What is factorial distribution,9341
1096,10071,"For many continuous random variables, we can define an extremely useful function with which to calculate probabilities of events associated to the random variable. In short, the PDF of a continuous random variable is the derivative of its CDF.",Is the derivative of the probability distribution function PDF just a cumulative distribution function cdf,10071
1097,3912,"Global max pooling = ordinary max pooling layer with pool size equals to the size of the input (minus filter size + 1, to be precise). You can see that MaxPooling1D takes a pool_length argument, whereas GlobalMaxPooling1D does not.",What is Global Max pooling,3912
1098,628,"Naive Bayes works best when you have small training data set, relatively small features(dimensions). If you have huge feature list, the model may not give you accuracy, because the likelihood would be distributed and may not follow the Gaussian or other distribution.",When should we use naive Bayes,628
1099,4135,Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making.  Also known as deep neural learning or deep neural network.,What is deep learning in simple words,4135
1100,4864,"At the point of non-differentiability, you can assign the derivative of the function at the point “right next” to the singularity and the algorithm will work fine. For example, in ReLU we can give the derivative of the function at zero as 0.",How do deep learning algorithms use ReLU if it is not differentiable at 0,4864
1101,7061,"In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies, such as the expression of a musical chord in terms of the volumes and frequencies of its constituent notes.",What is Fourier transform in signals and systems,7061
1102,567,User-Based Collaborative Filtering is a technique used to predict the items that a user might like on the basis of ratings given to that item by the other users who have similar taste with that of the target user. Many websites use collaborative filtering for building their recommendation system.,What is user based collaborative filtering,567
1103,872,"In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers.  It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.",What is Perceptron in machine learning,872
1104,721,"linear threshold unit (LTU) A linear threshold unit is a simple artificial neuron whose output is its thresholded total net input. That is, an LTU with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T.",What is a linear threshold unit,721
1105,4460,"Communalities – This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables.  They are the reproduced variances from the factors that you have extracted.",What is the meaning of communality in factor analysis,4460
1106,2902,"An autocorrelation plot is designed to show whether the elements of a time series are positively correlated, negatively correlated, or independent of each other. (The prefix auto means “self”— autocorrelation specifically refers to correlation among the elements of a time series.)",What is an autocorrelation plot,2902
1107,5004,The main requirements that a clustering algorithm should satisfy are:scalability;dealing with different types of attributes;discovering clusters with arbitrary shape;minimal requirements for domain knowledge to determine input parameters;ability to deal with noise and outliers;More items,What are the requirements of clustering algorithms,5004
1108,3830,The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement.  It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data.,What is bootstrapping in machine learning,3830
1109,1499,"While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope.  Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.",What is the difference between data science and data analytics,1499
1110,3817,"Backpropagation only works during training the model on a dataset.  You run your model with the learned parameters (from Backpropagation) and best hyperparameters (from validation) once on the Test set and report the accuracy. You never learn anything, be it parameters or hyperparameters on the Test set.",How does backpropagation work between training validation and test sets,3817
1111,5737,"Inverted dropout is a variant of the original dropout technique developed by Hinton et al. Just like traditional dropout, inverted dropout randomly keeps some weights and sets others to zero. In contrast, traditional dropout requires scaling to be implemented during the test phase.",What is inverted dropout,5737
1112,7253,"Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.",What does a normal distribution tell us,7253
1113,6383,"An environment is everything in the world which surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it.",What is agent and environment in artificial intelligence,6383
1114,7470,"Data are rarely randomly distributed in high-dimensions and are highly correlated, often with spurious correlations. The distances between a data point and its nearest and farthest neighbours can become equidistant in high dimensions, potentially compromising the accuracy of some distance-based analysis tools.",What are the implications of using highly dimensional data,7470
1115,5428,"Recall that in order for a neural networks to learn, weights associated with neuron connections must be updated after forward passes of data through the network. These weights are adjusted to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes.",How are weights updated in neural network,5428
1116,7834,Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.,Are generative models unsupervised,7834
1117,4306,"Covariance is when two variables vary with each other, whereas Correlation is when the change in one variable results in the change in another variable.Differences between Covariance and Correlation.CovarianceCorrelationCovariance can vary between -∞ and +∞Correlation ranges between -1 and +17 more rows•",Should I use correlation or covariance,4306
1118,10051,"A metric is a function that is used to judge the performance of your model. Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model. Note that you may use any loss function as a metric.",What is metrics in neural network,10051
1119,6440,Clustering starts by computing a distance between every pair of units that you want to cluster. A distance matrix will be symmetric (because the distance between x and y is the same as the distance between y and x) and will have zeroes on the diagonal (because every item is distance zero from itself).,What is distance matrix in clustering,6440
1120,3163,"load_data function Loads the MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. More info can be found at the MNIST homepage.",What is Mnist Load_data (),3163
1121,5898,"Dummy variables (sometimes called indicator variables) are used in regression analysis and Latent Class Analysis. As implied by the name, these variables are artificial attributes, and they are used with two or more categories or levels.",Why is it called a dummy variable,5898
1122,2042,"Probability density function (PDF) is a statistical expression that defines a probability distribution (the likelihood of an outcome) for a discrete random variable (e.g., a stock or ETF) as opposed to a continuous random variable.",What does probability density function mean,2042
1123,1644,Artificial neural networks are forecasting methods that are based on simple mathematical models of the brain. They allow complex nonlinear relationships between the response variable and its predictors.,What are neural network models,1644
1124,8064,"The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression.",Can neural networks be used for linear regression,8064
1125,10632,"To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions:Use a linear activation function for the final layer.Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values)",How can we make a neural network to predict a continuous variable,10632
1126,8410,"In statistics and probability, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one fewer quantile than the number of groups created.",What is quantile distribution,8410
1127,1811,Features: The characteristics that define your problem. These are also called attributes. Parameters: The variables your algorithm is trying to tune to build an accurate model.,What are the features and parameters in machine learning,1811
1128,6016,"Answer : Algorithm is a noun meaning some special process of solving a certain type of problem.  Whereas logarithm, again a noun, is the exponent of that power of a fixed number, called the base, which equals a given number, called the antilogarithm.",What is the difference between a logarithm and an algorithm,6016
1129,5381,"To find the interquartile range (IQR), ​first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.",How do you find the interquartile range,5381
1130,5587,"Once you have calculated the decimal values of each percentage for each given sample size, you then add these decimal values together and divide the total number by the total sum of both sample sizes. You then need to multiply this value by 100 to get the average percentage.5 days ago",How do you calculate the median percentage,5587
1131,4898,Stochastic Gradient Descent (SGD) addresses both of these issues by following the negative gradient of the objective after seeing only a single or a few training examples. The use of SGD In the neural network setting is motivated by the high cost of running back propagation over the full training set.,What is SGD in neural network,4898
1132,4193,Decision tree learning is generally best suited to problems with the following characteristics: Instances are represented by attribute-value pairs. There is a finite list of attributes (e.g. hair colour) and each instance stores a value for that attribute (e.g. blonde).,What type of problems are best suited for decision tree learning,4193
1133,2339,"1 Answers found. A recursive filter has a system in which the output is directly dependent on one or more of its past outputs. But in a non recursive filter the system followed is the one in which the output is independent of any of the past outputs like, the feed-forward system where the system is having no feedback.",What is the main difference between recursive and non recursive filters in DSP,2339
1134,345,"The calibration module allows you to better calibrate the probabilities of a given model, or to add support for probability prediction.  Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level.",What is calibrated probability,345
1135,10570,"So, assuming a 15% survey response rate, we see that you should send your NPS survey to 1,700 customers. What if you're a smaller company and don't have enough customers to send the recommended number of invitations?",What is a good sample size for NPS,10570
1136,136,"Fisher's exact test obtains its two-tailed P value by computing the probabilities associated with all possible tables that have the same row and column totals. Then, it identifies the alternative tables with a probability that is less than that of the observed table.",What is an intuitive explanation of the Fisher exact test,136
1137,2263,"The loss function of SVM is very similar to that of Logistic Regression. Looking at it by y = 1 and y = 0 separately in below plot, the black line is the cost function of Logistic Regression, and the red line is for SVM. Please note that the X axis here is the raw model output, θᵀx.",What is the loss function for SVM,2263
1138,2727,"This task of identifying the best subset of predictors to include in the model, among all possible subsets of predictors, is referred to as variable selection.",What is variable selection in regression,2727
1139,1160,"It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction.",What is a loss function machine learning,1160
1140,1186,"Standard Deviation: The Difference. The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean.",Is the standard deviation the error,1186
1141,7308,Criteria for CausalityStrength: A relationship is more likely to be causal if the correlation coefficient is large and statistically significant.Consistency: A relationship is more likely to be causal if it can be replicated.More items•,How do you know if correlation is causation,7308
1142,6925,"Stride is a parameter of the neural network's filter that modifies the amount of movement over the image or video. For example, if a neural network's stride is set to 1, the filter will move one pixel, or unit, at a time.",What is stride in deep learning,6925
1143,6141,"The hazard rate measures the propensity of an item to fail or die depending on the age it has reached. It is part of a wider branch of statistics called survival analysis, a set of methods for predicting the amount of time until a certain event occurs, such as the death or failure of an engineering system or component.",What is hazard rate in survival analysis,6141
1144,7560,"Linear discriminant function analysis (i.e., discriminant analysis) performs a multivariate test of differences between groups.  In addition, discriminant analysis is used to determine the minimum number of dimensions needed to describe these differences.",What is discriminant analysis in SPSS,7560
1145,10494,Path analysis is a special case of SEM.  Most of the models that you will see in the literature are SEM rather than path analyses. The main difference between the two types of models is that path analysis assumes that all variables are measured without error. SEM uses latent variables to account for measurement error.,What is the difference between path analysis and SEM,10494
1146,9934,The gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors.  The C parameter trades off correct classification of training examples against maximization of the decision function's margin.,What is C and gamma in SVM,9934
1147,641,"Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.",What is a deep learning model,641
1148,489,"Structured data is highly specific and is stored in a predefined format, where unstructured data is a conglomeration of many varied types of data that are stored in their native formats. This means that structured data takes advantage of schema-on-write and unstructured data employs schema-on-read.",What is structured and unstructured data,489
1149,7943,Random Forest Regression is a supervised learning algorithm that uses ensemble learning method for regression.  A Random Forest operates by constructing several decision trees during training time and outputting the mean of the classes as the prediction of all the trees.,What is a Random Forest Regression,7943
1150,3847,"Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account.  A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis.",What is the difference between prior and posterior probabilities,3847
1151,2252,"It is the sum of the likelihood residuals. At record level, the natural log of the error (residual) is calculated for each record, multiplied by minus one, and those values are totaled.",What is the log likelihood in logistic regression,2252
1152,2772,"A bias vector is an additional set of weights in a neural network that require no input, and this it corresponds to the output of an artificial neural network when it has zero input. Bias represents an extra neuron included with each pre-output layer and stores the value of “1,” for each action.",What is bias in convolutional neural network,2772
1153,10008,"In information theory, the graph entropy is a measure of the information rate achievable by communicating symbols over a channel in which certain pairs of values may be confused. This measure, first introduced by Körner in the 1970s, has since also proven itself useful in other settings, including combinatorics.",What is an entropy of Graph Is it related to concept of entropy in Information Theory,10008
1154,8140,"In spatial analysis, four major problems interfere with an accurate estimation of the statistical parameter: the boundary problem, scale problem, pattern problem (or spatial autocorrelation), and modifiable areal unit problem.  In analysis with area data, statistics should be interpreted based upon the boundary.",What are spatial problems,8140
1155,9090,"The binomial distribution model allows us to compute the probability of observing a specified number of ""successes"" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure.",What are binomial distributions used for,9090
1156,2363,"AUC and accuracy are fairly different things.  For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.",What is the difference between accuracy and AUC,2363
1157,6432,SGD is a variant of gradient descent. Instead of performing computations on the whole dataset — which is redundant and inefficient — SGD only computes on a small subset or random selection of data examples.  Essentially Adam is an algorithm for gradient-based optimization of stochastic objective functions.,What is the difference between SGD and Adam,6432
1158,10871,"It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance. In this tutorial, you will discover the rectified linear activation function for deep learning neural networks.",Why is the ReLU activation function used the most often in neural networks for computer vision,10871
1159,4447,"StepsStep 1: For each (x,y) point calculate x2 and xy.Step 2: Sum all x, y, x2 and xy, which gives us Σx, Σy, Σx2 and Σxy (Σ means ""sum up"")Step 3: Calculate Slope m:m = N Σ(xy) − Σx Σy N Σ(x2) − (Σx)2Step 4: Calculate Intercept b:b = Σy − m Σx N.Step 5: Assemble the equation of a line.",What is the least squares regression formula,4447
1160,4255,"The most common evaluation metric that is used in object recognition tasks is 'mAP', which stands for 'mean average precision'. It is a number from 0 to 100 and higher values are typically better, but it's value is different from the accuracy metric in classification.",What is mAP object detection,4255
1161,4981,"To calculate permutations, we use the equation nPr, where n is the total number of choices and r is the amount of items being selected. To solve this equation, use the equation nPr = n! / (n - r)!.",How do you calculate permutations,4981
1162,1600,Understanding the differences Detection refers to mining insights or information in a data pool when it is being processed.  Prediction or predictive analysis employs probability based on the data analyses and processing.,What is the difference between detection and prediction,1600
1163,5660,"Cluster sampling is best used when the clusters occur naturally in a population, when you don't have access to the entire population, and when the clusters are geographically convenient. However, cluster sampling is not as precise as simple random sampling or stratified random sampling.",When should cluster sampling be used,5660
1164,8161,"The mean value of x is thus the first moment of its distribution, while the fact that the probability distribution is normalized means that the zeroth moment is always 1.  The variance of x is thus the second central moment of the probability distribution when xo is the mean value or first moment.",What are moments prove that first moment is average and second moment is variance,8161
1165,4441,"Here are five ways, but it really all boils down to stretching your brain by learning new things:Become a renaissance man. Or woman.  Play the brain game Dual N-Back. Do this 20 minutes a day.  Do regular high cardio exercise.  Learn an instrument.  Buy the book Boost Your IQ by Carolyn Skitt, and play all the games.",How do you get genius level intelligence,4441
1166,6824,"Mean: the average score, calculated by dividing the sum of scores by the number of examinees.  Median: the middle raw score of the distribution; 50 percent of the obtained raw scores are higher and 50 percent are lower than the median.",What does distribution of scores mean,6824
1167,7411,"The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions.",How do you evaluate machine learning models,7411
1168,3710,"Word sense disambiguation, in natural language processing (NLP), may be defined as the ability to determine which meaning of word is activated by the use of word in a particular context.  Lexical ambiguity, syntactic or semantic, is one of the very first problem that any NLP system faces.",What is word sense disambiguation give example,3710
1169,3494,"Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation as first implemented by Allen.",Which algorithm is used in recommendation system,3494
1170,8602,"If the weights are zero, complexity of the whole deep net would be the same as that of a single neuron and the predictions would be nothing better than random. Nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights.",Is random weight assignment better than assigning weights to the units in the hidden layer,8602
1171,2873,"Sample variance Concretely, the naive estimator sums the squared deviations and divides by n, which is biased.  The sample mean, on the other hand, is an unbiased estimator of the population mean μ. Note that the usual definition of sample variance is. , and this is an unbiased estimator of the population variance.",Is the sample variance an unbiased estimator,2873
1172,3589,"The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.",What is Alpha in statistics significance level,3589
1173,7339,"The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters (at least in the first stage). In stratified sampling, the sampling is done on elements within each stratum.",What is the difference between cluster sampling and stratified sampling,7339
1174,8522,Train and serve a TensorFlow model with TensorFlow ServingTable of contents.Create your model. Import the Fashion MNIST dataset. Train and evaluate your model.Save your model.Examine your saved model.Serve your model with TensorFlow Serving. Add TensorFlow Serving distribution URI as a package source:  Make a request to your model in TensorFlow Serving. Make REST requests.,How do you use TensorFlow serving,8522
1175,6048,Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models.,What is data preprocessing in machine learning,6048
1176,10281,"In probability, and statistics, a multivariate random variable or random vector is a list of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value.  Normally each element of a random vector is a real number.",What is a multivariate variable,10281
1177,1238,To calculate the variance follow these steps:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result (the squared difference).Then work out the average of those squared differences. (Why Square?),How do you calculate sample variance,1238
1178,9929,"Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.",How does Poisson regression work,9929
1179,1166,Business Uses The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.,What is K means used for,1166
1180,8816,"Semantic similarity is calculated based on two semantic vectors. An order vector is formed for each sentence which considers the syntactic similarity between the sentences. Finally, semantic similarity is calculated based on semantic vectors and order vectors.",How do you find the semantic similarity between two words,8816
1181,3156,Batch normalization is a layer that allows every layer of the network to do learning more independently. It is used to normalize the output of the previous layers. The activations scale the input layer in normalization.,What is batch normalization CNN,3156
1182,10857,"SVD, or Singular Value Decomposition, is one of several techniques that can be used to reduce the dimensionality, i.e., the number of columns, of a data set.  SVD is an algorithm that factors an m x n matrix, M, of real or complex values into three component matrices, where the factorization has the form USV*.",How does SVD help in dimensionality reduction,10857
1183,2681,7.2. Radial basis function (RBF) networks are a commonly used type of artificial neural network for function approximation problems. Radial basis function networks are distinguished from other neural networks due to their universal approximation and faster learning speed.,Which function popularly we used in RBF network,2681
1184,4575,"Frequency is not quantized, and has a continuous spectrum. As such, a photon can have any energy, as E=ℏω. However, quantum mechanically, if a particle is restricted by a potential, i.e. for V≠0, the energy spectrum is discrete.",Is frequency quantized,4575
1185,9437,- Chad Orzel - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you find the Uncertainty Principle,9437
1186,10196,"The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution.",Why is distribution important in statistics,10196
1187,1091,Random field theory (RFT) is a recent body of mathematics defining theo- retical results for smooth statistical maps.  The way that RFT solves this problem is by using results that give the expected Euler characteristic (EC) for a smooth statistical map that has been thresholded.,What is random field theory,1091
1188,10649,"“The benefit to using a one-tailed test is that it requires fewer subjects to reach significance. A two-tailed test splits your significance level and applies it in both directions. Thus, each direction is only half as strong as a one-tailed test, which puts all the significance in one direction.",What is the primary benefit of conducting a one tailed test instead of a two tailed test,10649
1189,3432,"Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.",What are GANs in machine learning,3432
1190,6384,"Mean, variance, and standard deviation The mean of the sampling distribution of the sample mean will always be the same as the mean of the original non-normal distribution. In other words, the sample mean is equal to the population mean.",Is the sample mean equal to the population mean,6384
1191,10223,"Unconscious racial stereotypes are a major example of implicit bias. In other words, having an automatic preference for one race over another without even being aware of this bias.",What is implicit bias example,10223
1192,762,"Target Concept Term used in the machine learning literature to denote the Bayes decision rule, or the regression function, depending on the context. The target concept is a member of the concept space. Synonyms: Bayes Decision Rule in classification, Regression Function in regression.",What is the target concept in machine learning,762
1193,1483,"N-grams are contiguous sequences of n-items in a sentence. N can be 1, 2 or any other positive integers, although usually we do not consider very large N because those n-grams rarely appears in many different places.  This post describes several different ways to generate n-grams quickly from input sentences in Python.",What is N grams Python,1483
1194,10740,"Descriptive studies only describe the current state of a variable, so there are no presumed cause or effects, therefore no independent and dependent variables.  Since neither variable in a correlational design is manipulated, it is impossible to determine which is the cause and which is the effect.",Why are dependent and independent variables not applicable in a descriptive type of research,10740
1195,7228,"Dropout is a regularization technique for neural network models proposed by Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting (download the PDF). Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly.",What is dropout rate in deep learning,7228
1196,4134,"In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.",What is the condition for iterative method,4134
1197,4451,Altman's Z-Score model is a numerical measurement that is used to predict the chances of a business going bankrupt in the next two years. The model was developed by American finance professor Edward Altman in 1968 as a measure of the financial stability of companies.,What is Altman's Z score model,4451
1198,556,"A feedforward neural network is a biologically inspired classification algorithm. It consist of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer.  This is why they are called feedforward neural networks.",What is meant by feed forward neural network,556
1199,9765,"The chi-square test is a hypothesis test designed to test for a statistically significant relationship between nominal and ordinal variables organized in a bivariate table. In other words, it tells us whether two variables are independent of one another.  The chi-square test is sensitive to sample size.",Is Chi square bivariate analysis,9765
1200,6881,"For large samples, the sample proportion is approximately normally distributed, with mean μˆP=p. and standard deviation σˆP=√pqn. A sample is large if the interval [p−3σˆp,p+3σˆp] lies wholly within the interval [0,1].",How do you find a sample proportion,6881
1201,6792,"The ""Fast Fourier Transform"" (FFT) is an important measurement method in the science of audio and acoustics measurement. It converts a signal into individual spectral components and thereby provides frequency information about the signal.",What is the purpose of FFT,6792
1202,8225,The weaknesses of decision tree methods : Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.,What are the issues in decision tree induction,8225
1203,1929,"A one-sided argument (also known as card stacking, stacking the deck, ignoring the counterevidence, slanting, and suppressed evidence) is an informal fallacy that occurs when only the reasons supporting a proposition are supplied, while all reasons opposing it are omitted.",What fallacy involves a deliberate selection of data to support only one side of an issue,1929
1204,8562,The availability heuristic is a mental shortcut that helps us make a decision based on how easy it is to bring something to mind.  The representativeness heuristic is a mental shortcut that helps us make a decision by comparing information to our mental prototypes.,What is the difference between representative and availability heuristics,8562
1205,39,Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.,Why is learning rate used in gradient descent optimization,39
1206,5189,"Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set.",What exactly is variance,5189
1207,7173,"Normal Approximation to the Binomialn is your sample size,p is your given probability.q is just 1 – p. For example, let's say your probability p is . You would find q by subtracting this probability from 1: q = 1 – . 6 = .",How do you find the probability of normal approximation,7173
1208,355,"In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with",What is hypergeometric distribution in statistics,355
1209,161,"With binary data the variance is a function of the mean, and in particular is not constant as the mean changes. This violates one of the standard linear regression assumptions that the variance of the residual errors is constant.",Why linear regression is not suitable for modeling binary responses,161
1210,9218,"Quota sampling is different from stratified sampling, because in a stratified sample individuals within each stratum are selected at random. Quota sampling achieves a representative age distribution, but it isn't a random sample, because the sampling frame is unknown.",Is quota sampling random,9218
1211,9432,To see the accuracy of clustering process by using K-Means clustering method then calculated the square error value (SE) of each data in cluster 2. The value of square error is calculated by squaring the difference of the quality score or GPA of each student with the value of centroid cluster 2.,How do you find the accuracy of K means clustering,9432
1212,7628,"A disadvantage is when researchers can't classify every member of the population into a subgroup. Stratified random sampling is different from simple random sampling, which involves the random selection of data from the entire population so that each possible sample is equally likely to occur.",What are the disadvantages of stratified sampling,7628
1213,656,"Bias allows you to shift the activation function by adding a constant (i.e. the given bias) to the input. Bias in Neural Networks can be thought of as analogous to the role of a constant in a linear function, whereby the line is effectively transposed by the constant value.",What are biases in neural network,656
1214,10313,"Ridge and lasso regression allow you to regularize (""shrink"") coefficients. This means that the estimated coefficients are pushed towards 0, to make them work better on new data-sets (""optimized for prediction""). This allows you to use complex models and avoid over-fitting at the same time.",Why do we use Ridge and lasso regression,10313
1215,3916,Endogenous variables are used in econometrics and sometimes in linear regression. They are similar to (but not exactly the same as) dependent variables. Endogenous variables have values that are determined by other variables in the system (these “other” variables are called exogenous variables).,How do you find endogenous variables,3916
1216,9541,"The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. The inverted file may be the database file itself, rather than its index.",What are the uses of an inverted index,9541
1217,3288,"Because it arises from consistency between parts of a test, split-half reliability is an “internal consistency” approach to estimating reliability. This result is an estimate of the reliability of the test scores, and it provides some support for the quality of the test scores.",Why is split half reliability important,3288
1218,9738,The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1.,What is the difference between logit and probit regression,9738
1219,7415,"Here are 11 tips for making the most of your large data sets.Cherish your data. “Keep your raw data raw: don't manipulate it without having a copy,” says Teal.  Visualize the information.Show your workflow.  Use version control.  Record metadata.  Automate, automate, automate.  Make computing time count.  Capture your environment.More items•",How do you handle large datasets,7415
1220,1748,We call vectorization the general process of turning a collection of text documents into numerical feature vectors.  Documents are described by word occurrences while completely ignoring the relative position information of the words in the document.,What is feature vectorization,1748
1221,5010,"Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution. The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by *.",What is convolution in an image,5010
1222,5790,"R is a very dynamic and versatile programming language for data science. This article deals with classification in R. Generally classifiers in R are used to predict specific category related information like reviews or ratings such as good, best or worst. Various Classifiers are: Decision Trees.",What is R classification,5790
1223,563,"Leaky ReLU & Parametric ReLU (PReLU) Leaky ReLU has two benefits: It fixes the “dying ReLU” problem, as it doesn't have zero-slope parts. It speeds up training. There is evidence that having the “mean activation” be close to 0 makes training faster.",What are the advantages of using Leaky Rectified Linear Units Leaky ReLU over normal ReLU in deep learning,563
1224,9662,"7 Practical Guidelines for Accurate Statistical Model BuildingRemember that regression coefficients are marginal results.  Start with univariate descriptives and graphs.  Next, run bivariate descriptives, again including graphs.  Think about predictors in sets.  Model building and interpreting results go hand-in-hand.More items",How do you develop a statistical model,9662
1225,5156,A population is called multinomial if its data is categorical and belongs to a collection of discrete non-overlapping classes. The null hypothesis for goodness of fit test for multinomial distribution is that the observed frequency fi is equal to an expected count ei in each category.,What is multinomial population,5156
1226,4808,"The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean.  The lower the value of the coefficient of variation, the more precise the estimate.",Is it better to have a higher or lower coefficient of variation,4808
1227,5269,"Two determine if two images are rotated versions of each other, one can either exhaustively rotate them in order to find out if the two match up at some angle, or alternatively extract features from the images that can then be compared to make the same decision.",What is rotation invariant in image processing,5269
1228,826,"Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data.",What do you mean by linear model,826
1229,6994,"Three reasons that you should NOT use deep learning(1) It doesn't work so well with small data. To achieve high performance, deep networks require extremely large datasets.  (2) Deep Learning in practice is hard and expensive. Deep learning is still a very cutting edge technique.  (3) Deep networks are not easily interpreted.",When should you not use deep learning,6994
1230,4734,"Batch processing requires separate programs for input, process and output.  In contrast, real time data processing involves a continual input, process and output of data. Data must be processed in a small time period (or near real time). Radar systems, customer services and bank ATMs are examples.",What is the difference between batch processing and real time processing,4734
1231,4252,Step 1: Load Python packages.  Step 2: Pre-Process the data.  Step 3: Subset the data.  Step 4: Split the data into train and test sets.  Step 5: Build a Random Forest Classifier.  Step 6: Predict.  Step 7: Check the Accuracy of the Model.  Step 8: Check Feature Importance.,How do you create a classification model,4252
1232,1541,"The “trick” is that kernel methods represent the data only through a set of pairwise similarity comparisons between the original data observations x (with the original coordinates in the lower dimensional space), instead of explicitly applying the transformations ϕ(x) and representing the data by these transformed",What is the kernel trick SVM,1541
1233,2952,"If I know a programming language, where is a great place to start practicing algorithms?  Become proficient at written communication.  Learn Functional Programming.  Learn Object Oriented Analysis and Design.  Free Code Camp.More items•",How can I begin to learn algorithms,2952
1234,2796,"For an idea we are all familiar with, randomness is surprisingly hard to formally define. We think of a random process as something that evolves over time but in a way we can't predict.",Can we predict randomness,2796
1235,782,The difference between the two norms is that the standard deviation is calculating the square of the difference whereas the mean absolute deviation is only looking at the absolute difference. Hence large outliers will create a higher dispersion when using the standard deviation instead of the other method.,What is the logical difference between mean deviation and standard deviation,782
1236,8418,"Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks.  Spreading activation can also be applied in information retrieval, by means of a network of nodes representing documents and terms contained in those documents.",What does spreading activation mean,8418
1237,6263,"Convolution is used in the mathematics of many fields, such as probability and statistics. In linear systems, convolution is used to describe the relationship between three signals of interest: the input signal, the impulse response, and the output signal.",What is the use of convolution,6263
1238,8485,"In image processing, thresholding is used to split an image into smaller segments, or junks, using at least one color or gray scale value to define their boundary. The advantage of obtaining first a binary image is that it reduces the complexityof the data and simplifies the process of recognition and classification.",Why thresholding is used in image processing,8485
1239,5955,The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works.,What is canny edge detection in image processing,5955
1240,5542,"The geometric distribution would represent the number of people who you had to poll before you found someone who voted independent. You would need to get a certain number of failures before you got your first success. If you had to ask 3 people, then X=3; if you had to ask 4 people, then X=4 and so on.",What does a geometric distribution look like,5542
1241,8100,"Measurement uncertainty is critical to risk assessment and decision making. Organizations make decisions every day based on reports containing quantitative measurement data. If measurement results are not accurate, then decision risks increase. Selecting the wrong suppliers, could result in poor product quality.",Why do statistics and uncertainties matter,8100
1242,4386,"In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged).",Is deep learning a part of machine learning,4386
1243,3636,Time Series Forecast in RStep 1: Reading data and calculating basic summary.  Step 2: Checking the cycle of Time Series Data and Plotting the Raw Data.  Step 3: Decomposing the time series data.  Step 4: Test the stationarity of data.  Step 5: Fitting the model.  Step 6: Forecasting.,How do you forecast time series data,3636
1244,2665,Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data.,How does machine learning collect data,2665
1245,1207,"The hazard rate refers to the rate of death for an item of a given age (x). It is part of a larger equation called the hazard function, which analyzes the likelihood that an item will survive to a certain point in time based on its survival to an earlier time (t).",What is the hazard rate function,1207
1246,8981,The normal distribution is a probability function that describes how the values of a variable are distributed. It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions.,Which distribution is a normal distribution,8981
1247,6540,"On the other hand, when the normal approximation is used to approximate a discrete distribution, a continuity correction can be employed so that we can approximate the probability of a specific value of the discrete distribution. The continuity correction requires adding or subtracting .",Why is the correction for continuity used when using the normal approximation to the binomial distribution,6540
1248,8075,"It is a particular Monte Carlo method that numerically computes a definite integral. While other algorithms usually evaluate the integrand at a regular grid, Monte Carlo randomly chooses points at which the integrand is evaluated. This method is particularly useful for higher-dimensional integrals.",How does Monte Carlo integration work,8075
1249,2528,"Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.",Why is there a Bayesian network,2528
1250,7493,"Although side effects believed to be caused by statins can be annoying, consider the benefits of taking a statin before you decide to stop taking your medication. Remember that statin medications can reduce your risk of a heart attack or stroke, and the risk of life-threatening side effects from statins is very low.",Are statins really worth taking,7493
1251,8382,2. Why is it important to examine a residual plot even if a scatterplot appears to be linear? An examination of the of the residuals often leads us to discover groups of observations that are different from the rest.,Why is it important to examine a residual plot,8382
1252,3301,"Prediction bias is a quantity that measures how far apart those two averages are. That is: prediction bias = average of predictions − average of labels in data set. Note: ""Prediction bias"" is a different quantity than bias (the b in wx + b).",What is bias in classification,3301
1253,5668,"The sample proportion, P is an unbiased estimator of the population proportion, . Unbiased estimators determines the tendency , on the average, for the statistics to assume values closed to the parameter of interest.",Is proportion a biased estimator,5668
1254,363,Jakob Bernoulli,Who discovered the law of large numbers,363
1255,1805,"The top 5 AI developments as chosen by our team are as follows:The increased speed of AI-enabled medical research.  Computer vision, image, and video analysis technology is evolving.  Powerful AI-based tools become mainstream.  AI learns increasingly higher-level human functions.More items•",What are the recent developments in AI,1805
1256,2985,"Sampling is done because you usually cannot gather data from the entire population. Even in relatively small populations, the data may be needed urgently, and including everyone in the population in your data collection may take too long.",Why is sample survey done,2985
1257,3720,"Association rule mining is a procedure which aims to observe frequently occurring patterns, correlations, or associations from datasets found in various kinds of databases such as relational databases, transactional databases, and other forms of repositories.",What is an association rule in data mining,3720
1258,7893,"From Wikipedia, the free encyclopedia. Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items.",What does Kappa mean in statistics,7893
1259,6632,"Concepts in Machine Learning can be thought of as a boolean-valued function defined over a large set of training data.  We have some attributes/features of the day like, Sky, Air Temperature, Humidity, Wind, Water, Forecast and based on this we have a target Concept named EnjoySport.",What is ML concept,6632
1260,5746,It is one of the more common descriptive statistics functions used to calculate uncertainty.How to CalculateSubtract each value from the mean.Square each value in step 1.Add all of the values from step 2.Count the number of values and Subtract it by 1.Divide step 3 by step 4.Calculate the Square Root of step 5.,How do you calculate uncertainty in statistics,5746
1261,3965,"One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture.",Which of the following were introduced to overcome the vanishing gradient problem,3965
1262,6999,K-nearest neighbor is also used in retail to detect patterns in credit card usage. Many new transaction-scrutinizing software applications use kNN algorithms to analyze register data and spot unusual patterns that indicate suspicious activity.,What are industry applications of the K nearest neighbor algorithm,6999
1263,5182,"One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture.",What is the vanishing gradient problem and how do we overcome that,5182
1264,10617,"Symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level ""symbolic"" (human-readable) representations of problems, logic and search.  Production rules connect symbols in a relationship similar to an If-Then statement.",What is symbolic machine learning,10617
1265,3639,"Definition: The trend is the component of a time series that represents variations of low frequency in a time series, the high and medium frequency fluctuations having been filtered out.",What is trend in time series data,3639
1266,381,"Machine learning can be described in many ways. Perhaps the most useful is as type of optimization.  This is done via what is known as an objective function, with “objective” used in the sense of a goal. This function, taking data and model parameters as arguments, can be evaluated to return a number.",What is an objective function in machine learning,381
1267,7592,"However, it is not necessary for you to learn the machine learning algorithms that are not a part of machine learning in order to learn deep learning. Instead, if you want to learn deep learning then you can go straight to learning the deep learning models if you want to.",Can I directly learn deep learning,7592
1268,8018,Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items.,What is Cohen's kappa used for,8018
1269,3147,"Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set.  Based on historical data about earlier outcomes involving the same input criteria, it then scores new cases on their probability of falling into a particular outcome category.",What is logistic regression in data science,3147
1270,880,"Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors.  12 ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests.",What is the concept of analysis of variance ANOVA in statistics,880
1271,3463,"The aim of distributional semantics is to learn the meanings of linguistic expressions from a corpus of text. The core idea, known as the distributional hy- pothesis, is that the contexts in which an expression appears give us information about its meaning.",What are the goals of distributional semantics,3463
1272,8664,"Midrange determines the number that is halfway between the minimum and maximum numbers of a data set. It is a statistical tool that identifies a measure of center like median, mean or mode.",What is the mid range in statistics,8664
1273,8719,Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems.,What is Adam algorithm,8719
1274,10422,"The least squares approach limits the distance between a function and the data points that the function explains. It is used in regression analysis, often in nonlinear regression modeling in which a curve is fit into a set of data. Mathematicians use the least squares method to arrive at a maximum-likelihood estimate.",Why do we use least square method,10422
1275,10605,"Heuristics are the ""shortcuts"" that humans use to reduce task complexity in judgment and choice, and biases are the resulting gaps between normative behavior and the heuristically determined behavior (Kahneman et al., 1982).",What is the difference between a heuristic and a bias,10605
1276,5916,"The coefficient of determination can also be found with the following formula: R2 = MSS/TSS = (TSS − RSS)/TSS, where MSS is the model sum of squares (also known as ESS, or explained sum of squares), which is the sum of the squares of the prediction from the linear regression minus the mean for that variable; TSS is the",What is the formula for calculating the coefficient of determination,5916
1277,4244,"Mutual information is a quantity that measures a relationship between two random variables that are sampled simultaneously. In particular, it measures how much information is communicated, on average, in one random variable about another.",How does mutual information work,4244
1278,1225,The three main methods to perform linear regression analysis in Excel are: Regression tool included with Analysis ToolPak. Scatter chart with a trendline.,What method does Excel use for linear regression,1225
1279,509,The Hidden layer of the neural network is the intermediate layer between Input and Output layer. Activation function applies on hidden layer if it is available.  Hidden nodes or hidden neurons are the neurons that are neither in the input layer nor the output layer [3].,What are hidden nodes in neural network,509
1280,5923,"Linear regression attempts to model the relationship between two variables by fitting a linear equation (= a straight line) to the observed data. One variable is considered to be an explanatory variable (e.g. your income), and the other is considered to be a dependent variable (e.g. your expenses).",How would linear regression be described and explained in laymans terms,5923
1281,10246,"The independent variable is the variable the experimenter changes or controls and is assumed to have a direct effect on the dependent variable.  The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.",What is the independent variable in an experiment,10246
1282,3671,"A Gaussian filter is a linear filter. It's usually used to blur the image or to reduce noise. If you use two of them and subtract, you can use them for ""unsharp masking"" (edge detection). The Gaussian filter alone will blur edges and reduce contrast.",What does a Gaussian filter do,3671
1283,10292,"If you don't know your population mean (μ) but you do know the standard deviation (σ), you can find a confidence interval for the population mean, with the formula: x̄ ± z* σ / (√n),  Step 1: Subtract the confidence level (Given as 95 percent in the question) from 1 and then divide the result by two.",How are confidence intervals calculated,10292
1284,9941,"""A discrete variable is one that can take on finitely many, or countably infinitely many values"", whereas a continuous random variable is one that is not discrete, i.e. ""can take on uncountably infinitely many values"", such as a spectrum of real numbers.",What is the difference between discrete and continuous variables,9941
1285,3080,"The sensitivity of the test reflects the probability that the screening test will be positive among those who are diseased. In contrast, the specificity of the test reflects the probability that the screening test will be negative among those who, in fact, do not have the disease.",How do you interpret sensitivity and specificity,3080
1286,5067,"Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games (e.g. Atari, Mario), with performance on par with or even exceeding humans.",What is reinforcement learning algorithms,5067
1287,3444,"For a discrete random variable, the expected value, usually denoted as or , is calculated using: μ = E ( X ) = ∑ x i f ( x i )",How do you find the expected value of a random variable,3444
1288,9540,"The notation for the uniform distribution is X ~ U(a, b) where a = the lowest value of x and b = the highest value of x. The probability density function is f(x)=1b−a f ( x ) = 1 b − a for a ≤ x ≤ b. For this example, X ~ U(0, 23) and f(x)=123−0 f ( x ) = 1 23 − 0 for 0 ≤ X ≤ 23.",How do you calculate uniform distribution,9540
1289,6753,"A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut.",What is decision boundary in SVM,6753
1290,5708,"Propositional Logic converts a complete sentence into a symbol and makes it logical whereas in First-Order Logic relation of a particular sentence will be made that involves relations, constants, functions, and constants.",What is the difference between first order logic and propositional logic,5708
1291,1451,"The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.",What data is used to train vector space models of words such as word2vec,1451
1292,5164,4:1213:02Suggested clip · 101 secondsThe Transition Matrix - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you read a transition matrix,5164
1293,1286,"Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources.",What is predictive analytics used for,1286
1294,5183,Wilcoxon – The Wilcoxon signed rank test has the null hypothesis that both samples are from the same population.  Sign – The sign test has the null hypothesis that both samples are from the same population. The sign test compares the two dependent observations and counts the number of negative and positive differences.,What is the difference between sign test and Wilcoxon signed rank test,5183
1295,5850,"Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients.  On the other hand, L2 regularization (e.g. Ridge regression) doesn't result in elimination of coefficients or sparse models. This makes the Lasso far easier to interpret than the Ridge.",How are Lasso and ridge regressions used for regularization,5850
1296,7610,"The degree of freedom is not a property of the distribution, it's the name of the distribution. It refers to the number of degrees of freedom of some variable that has the distribution.",Statistics academic discipline What is an intuitive explanation of degrees of freedom,7610
1297,7149,"Mathematically speaking, a decision tree has low bias and high variance. Averaging the result of many decision trees reduces the variance while maintaining that low bias. Combining trees is known as an 'ensemble method'.",Is decision tree an ensemble method,7149
1298,7809,Feature Selection. Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.,What is feature selection and feature extraction,7809
1299,20,"In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications. Objects detections, recognition faces etc., are some of the areas where CNNs are widely used.",What is convolutional neural network in image processing,20
1300,2678,"Any object, function, or statistic that doesn't change when scales are multiplied by a common factor is scale invariant. In statistics, it can also mean a statistic that tends not to change (i.e. 99% of the time, it will stay the same). Some specific statistics are scale invariant.",What is invariance in statistics,2678
1301,4875,"Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data.",What is a linear model used for,4875
1302,10708,Each class will have a “lower class limit” and an “upper class limit” which are the lowest and highest numbers in each class. The “class width” is the distance between the lower limits of consecutive classes.,What is the difference between class size and class width,10708
1303,3021,A Binomial Regression model can be used to predict the odds of an event.  The Logistic Regression model is a special case of the Binomial Regression model in the situation where the size of each group of explanatory variables in the data set is one.,Is binomial regression the same as logistic regression,3021
1304,4367,Any good analysis of survey data from a stratified sample includes the same seven steps:Estimate a population parameter.Compute sample variance within each stratum.Compute standard error.Specify a confidence level.Find the critical value (often a z-score or a t-score).Compute margin of error.More items,How do you analyze stratified random sampling,4367
1305,544,"In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success/yes/true/one (with probability p)",What are the parameters of binomial distribution,544
1306,8473,"The name 'exponential smoothing' is attributed to the use of the exponential window function during convolution. It is no longer attributed to Holt, Winters & Brown. , and the weights assigned to previous observations are proportional to the terms of the geometric progression. .",Why is it called exponential smoothing,8473
1307,8732,"The biggest flaw in this machine learning technique, according to Mittu, is that there is a large amount of art to building these networks, which means there are few scientific methods to help understand when they will fail.",What's wrong with deep learning,8732
1308,7785,This lesson explains how to conduct a chi-square goodness of fit test. The test is applied when you have one categorical variable from a single population. It is used to determine whether sample data are consistent with a hypothesized distribution.,What does the chi square goodness of fit test actually test,7785
1309,8647,"Examples of Discrete Distribution The most common discrete probability distributions include binomial, Poisson, Bernoulli, and multinomial.",Which is an example of a discrete distribution,8647
1310,296,"Entropy is simply a measure of disorder and affects all aspects of our daily lives. In fact, you can think of it as nature's tax. Left unchecked disorder increases over time. Energy disperses, and systems dissolve into chaos.",What is entropy and why is it important,296
1311,2876,In Computer science (especially Machine learning) Pruning means simplifying/compressing and optimizing a Decision tree by removing sections of the tree that are uncritical and redundant to classify instances.,How does pruning work in decision trees,2876
1312,8759,"In such a sequence of trials, the geometric distribution is useful to model the number of failures before the first success. The distribution gives the probability that there are zero failures before the first success, one failure before the first success, two failures before the first success, and so on.",What is geometric distribution used for,8759
1313,4147,An ordinal variable is a categorical variable for which the possible values are ordered. Ordinal variables can be considered “in between” categorical and quantitative variables. Thus it does not make sense to take a mean of the values.,What type of variable is ordinal,4147
1314,5346,One of the key methodologies to improve efficiency in computational intensive tasks is to reduce the dimensions after ensuring most of the key information is maintained. It also eliminates features with strong correlation between them and reduces over-fitting.,When would you reduce dimensions in your data in machine learning,5346
1315,560,"The Antardasha of Mercury with Ketu Mahadasha can be evil and good depending on the placement of both Mercury and Ketu in the birth chart.  The antardasha of Mercury with Mahadasha of Ketu brings very bad results if the planet Mercury is weak, afflicted, aspect by Rahu, Saturn and Mars.",Is Ketu Mahadasha bad,560
1316,7081,"In probability theory and statistics, the exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate.",What is the mean of exponential distribution,7081
1317,255,"The main difference between the t-test and f-test is, that t-test is used to test the hypothesis whether the given mean is significantly different from the sample mean or not. On the other hand, an F-test is used to compare the two standard deviations of two samples and check the variability.",What is the difference between F and T test,255
1318,3629,"The range can also be used to estimate another measure of spread, the standard deviation. Rather than go through a fairly complicated formula to find the standard deviation, we can instead use what is called the range rule. The range is fundamental in this calculation.",What are the uses of the range in statistics and what are the areas that we use range for calculations in statistics,3629
1319,143,Grid-searching is the process of scanning the data to configure optimal parameters for a given model.  Grid-searching can be applied across machine learning to calculate the best parameters to use for any given model.,What is grid search in machine learning,143
1320,1624,The P-value is the probability that a chi-square statistic having 2 degrees of freedom is more extreme than 19.58. We use the Chi-Square Distribution Calculator to find P(Χ2 > 19.58) = 0.0001.,What is the relationship between p value and chi square,1624
1321,3762,"Discriminant analysis is a versatile statistical method often used by market researchers to classify observations into two or more groups or categories. In other words, discriminant analysis is used to assign objects to one group among a number of known groups.",What is the purpose of discriminant analysis,3762
1322,3353,"Preventing the error gradients from vanishing The presence of the forget gate's activations allows the LSTM to decide, at each time step, that certain information should not be forgotten and to update the model's parameters accordingly. and the gradient doesn't vanish.",How does LSTM help prevent the vanishing and exploding gradient problem in a recurrent neural network,3353
1323,9416,"Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time — for example, shipping movements across a geographic area over time (see above example image).",What is spatiotemporal analysis,9416
1324,10980,"The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape).",When would you use a Mann Whitney U test,10980
1325,1744,How to optimize your meta tags: A checklistCheck whether all your pages and your content have title tags and meta descriptions.Start paying more attention to your headings and how you structure your content.Don't forget to mark up your images with alt text.More items•,How do you optimize meta tags,5386
1326,9853,"In mathematics, the geometric–harmonic mean M(x, y) of two positive real numbers x and y is defined as follows: we form the geometric mean of g0 = x and h0 = y and call it g1, i.e. g1 is the square root of xy.  The geometric–harmonic mean is also designated as the harmonic–geometric mean. (cf. Wolfram MathWorld below.)",What is geometric mean and harmonic mean in statistics,9853
1327,565," Birst employs caching and aggregate awareness to send queries to the cache first, and then data to the user-ready data store.  If data is not cached, Birst generates one or more queries depending on how the data is sourced.  Birst's in-memory caching includes both exact and fuzzy matching.",What are the matching types that birst employs while searching data in cache,565
1328,2326,"The mean used here is referred to as the arithmetic mean – the sum of all values divided by the number of cases. When working with grouped data, this mean is sometimes referred to as the weighted mean or, more properly, the weighted arithmetic mean. Ungrouped and group methods.",Is a weighted mean and a grouped data mean the same,2326
1329,6930,"The most used algorithm to train neural networks is gradient descent. We'll define it later, but for now hold on to the following idea: the gradient is a numeric calculation allowing us to know how to adjust the parameters of a network in such a way that its output deviation is minimized.",What is gradient neural network,6930
1330,6948,7:5214:07Suggested clip · 100 secondsHow to Select the Correct Predictive Modeling Technique | Machine YouTubeStart of suggested clipEnd of suggested clip,How do you choose a predictive model,6948
1331,1688,"parameter-list is the list of parameters that the function takes separated by commas. If no parameters are given, then the function does not take any and should be defined with an empty set of parenthesis or with the keyword void. If no variable type is in front of a variable in the paramater list, then int is assumed.",What is the difference between a function with parameters and a functions without parameters,1688
1332,2714,"Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you're trying to analyze, the features you include in your dataset can vary widely.",What is a feature variable,2714
1333,4114,"Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions.",What are the uses of statistics,4114
1334,3761,Univariate statistics summarize only one variable at a time. Bivariate statistics compare two variables. Multivariate statistics compare more than two variables.,What is the difference between univariate and multivariate analysis,3761
1335,8405,The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1.,What is the difference between logit and probit model,8405
1336,9709,"Last Updated on Decem. Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions.",What is entropy and cross entropy,9709
1337,854,"A statistical hypothesis is a formal claim about a state of nature structured within the framework of a statistical model. For example, one could claim that the median time to failure from (acce]erated) electromigration of the chip population described in Section 6.1.",What is hypothesis in statistics with example,854
1338,8220,Euclidean distance,Which distance metric can be used in Knn,8220
1339,9994,TL;DR – The train_test_split function is for splitting a single dataset for two different purposes: training and testing.,Which of the following functions can be used to split the data into train and test,9994
1340,6157,"From Wikipedia, the free encyclopedia. An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B.",What is or in statistics,6157
1341,9255,"Perceptron Learning Rule The Perceptron receives multiple input signals, and if the sum of the input signals exceeds a certain threshold, it either outputs a signal or does not return an output. In the context of supervised learning and classification, this can then be used to predict the class of a sample.",How does a perceptron algorithm work,9255
1342,5976,A discrete variable is a variable whose value is obtained by counting. A continuous variable is a variable whose value is obtained by measuring.  A discrete random variable X has a countable number of possible values.,What are the differences between continuous and discrete variables,5976
1343,6133,A box plot (also known as box and whisker plot) is a type of chart often used in explanatory data analysis to visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages.,What is Boxplot used for,6133
1344,6038,"H is the measurement matrix. This matrix influences the Kalman Gain.  R is the sensor noise matrix. This matrix implies the measurement error covariance, based on the amount of sensor noise. In this simulation, Q and R are constants, but some implementations of the Kalman Filter may adjust them throughout execution.",What is H in Kalman filter,6038
1345,9910,"In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.",What is the purpose of probability distribution functions,9910
1346,7225,"An RNNs is essentially a fully connected neural network that contains a refactoring of some of its layers into a loop.  Among the text usages, the following tasks are among those RNNs perform well at: Sequence labelling. Natural Language Processing (NLP) text classification.",Is NLP neural network,7225
1347,10595,"Chi-Square goodness of fit test is a non-parametric test that is used to find out how the observed value of a given phenomena is significantly different from the expected value.  In Chi-Square goodness of fit test, sample data is divided into intervals.",What is the chi square goodness of fit test used for,10595
1348,9931,"Starting at $99.00 USD per user per month. Single-user, desktop application for Windows and Macs. Includes 12 months of technical support. Pricing information for IBM SPSS Statistics is supplied by the software provider or retrieved from publicly accessible pricing materials.",How much is SPSS for Mac,9931
1349,37,"From Wikipedia, the free encyclopedia. The control variates method is a variance reduction technique used in Monte Carlo methods. It exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.",What is control variates in variance reduction,37
1350,4654,"An autoencoder accepts input, compresses it, and then recreates the original input.  A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution.",What is the difference between traditional Autoencoder and variational Autoencoder,4654
1351,8084,Other examples that may follow a Poisson distribution include the number of phone calls received by a call center per hour and the number of decay events per second from a radioactive source.,What is Poisson distribution example,8084
1352,3076,"If a problem is nonlinear and its class boundaries cannot be approximated well with linear hyperplanes, then nonlinear classifiers are often more accurate than linear classifiers. If a problem is linear, it is best to use a simpler linear classifier.",What is linear and nonlinear classifier,3076
1353,5478,How do I run a Z Test?State the null hypothesis and alternate hypothesis.Choose an alpha level.Find the critical value of z in a z table.Calculate the z test statistic (see below).Compare the test statistic to the critical z value and decide if you should support or reject the null hypothesis.,How do you use Z test,5478
1354,8587,Probability is the chance of an event occurring. A probability distribution is a table or an equation that links each outcome of a statistical experiment with its probability of occurrence.,What is the difference between probability and probability distribution,8587
1355,7718,"In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.",What is a multiple hypothesis testing problem in statistics,7718
1356,4286,"The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation. These lower-dimensional vectors are non-negative which also means their coefficients are non-negative. Using the original matrix (A), NMF will give you two matrices (W and H).",How does NMF topic modeling work,4286
1357,5932,"Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply.",What is rule based learning in AI,5932
1358,10040,"It is not rare that the results from a study that uses a convenience sample differ significantly with the results from the entire population.  Since the sample is not representative of the population, the results of the study cannot speak for the entire population. This results to a low external validity of the study.",How does convenience sampling affect results,10040
1359,2370,If we use non - standard units then we may not be able to express our measurement internationally as mainly standard units are used and accepted internationally. The non- standard units do not have the same dimensions all over the world.,What are the disadvantages of using non standard units,2370
1360,3983,Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models.,What is preprocessing in machine learning,3983
1361,6397,"Static Rules Approach. The most simple, and maybe the best approach to start with, is using static rules. The Idea is to identify a list of known anomalies and then write rules to detect those anomalies. Rules identification is done by a domain expert, by using pattern mining techniques, or a by combination of both.",How do you do anomaly detection,6397
1362,1986,"Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true .",What is the log loss function,1986
1363,7673,"In a normal distribution, the mean and the median are the same number while the mean and median in a skewed distribution become different numbers: A left-skewed, negative distribution will have the mean to the left of the median. A right-skewed distribution will have the mean to the right of the median.",What are some ways to distinguish between a skewed and normal distribution,7673
1364,6670,The standard score (more commonly referred to as a z-score) is a very useful statistic because it (a) allows us to calculate the probability of a score occurring within our normal distribution and (b) enables us to compare two scores that are from different normal distributions.,What is Z score used for,6670
1365,1336,"The expression double standard originally referred to 18th- and 19th-century economic policies of bimetallism. Bimetallism was a monetary system that was based on two metals—a double standard, in its financial “prescribed value” sense, of gold and silver.",Where do double standards come from,1336
1366,5368,"matrix: A rectangular arrangement of numbers or terms having various uses such as transforming coordinates in geometry, solving systems of linear equations in linear algebra and representing graphs in graph theory.",What does a matrix represent in linear algebra,5368
1367,6649,"Homogeneity of variance is an assumption underlying both t tests and F tests (analyses of variance, ANOVAs) in which the population variances (i.e., the distribution, or “spread,” of scores around the mean) of two or more samples are considered equal.",What is homogeneity of variance in statistics,6649
1368,10448,"To convert a logit ( glm output) to probability, follow these 3 steps:Take glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) .",How do you convert odds ratio to logit,10448
1369,742,"The recommended reference range of serum TNF-α was from nondetectable to 8.1 pg/mL. Among 147 patients with IgAN, 98 patients were with elevated serum TNF-α and 49 patients were without elevated serum TNF-α.",What is normal range of TNF alpha,742
1370,1192,"Multiclass classification: classification task with more than two classes. Each sample can only be labelled as one class. For example, classification using features extracted from a set of images of fruit, where each image may either be of an orange, an apple, or a pear.",What is multi class classification in machine learning,1192
1371,9955,"When the image goes through them, the important features are kept in the convolution layers, and thanks to the pooling layers, these features are intensified and kept over the network, while discarding all the information that doesn't make a difference for the task.",What is convolution and pooling,9955
1372,9861,"Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.",When should logistic regression be used for data analysis,9861
1373,8883,"Second-Order/Pseudo-Second-Order Reaction For a Pseudo-Second-Order Reaction, the reaction rate constant k is replaced by the apparent reaction rate constant k'. If the reaction is not written out specifically to show a value of νA, the value is assumed to be 1 and is not shown in these equations.",What is a pseudo second order reaction,8883
1374,8919,"The values of the kernel filters are learned automatically by the neural network through the training process, and the filters kernels which results in the features that are most efficient for the particular classification or the detection are automatically learned.",How are filters chosen in CNN,8919
1375,6896,"The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed.  In general, a lower RMSD is better than a higher one.",What is root mean square error used for,6896
1376,4802,A recursive system is a system in which current output depends on previous output(s) and input(s) but in non-recursive system current output does not depend on previous output(s).,What is recursive and nonrecursive system,4802
1377,10858,"Fourier analysis is used in electronics, acoustics, and communications. Many waveforms consist of energy at a fundamental frequency and also at harmonic frequencies (multiples of the fundamental). The relative proportions of energy in the fundamental and the harmonics determines the shape of the wave.",What is Fourier analysis used for,10858
1378,683,"Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy.",How is Q learning off policy,683
1379,3297,K-nearest neighbors K- nearest neighbor (kNN) is a simple supervised machine learning algorithm that can be used to solve both classification and regression problems. kNN stores available inputs and classifies new inputs based on a similar measure i.e. the distance function.,Which algorithm is used for both classification and regression predictive problems,3297
1380,917,"The linear, polynomial and RBF or Gaussian kernel are simply different in case of making the hyperplane decision boundary between the classes.  Usually linear and polynomial kernels are less time consuming and provides less accuracy than the rbf or Gaussian kernels.",What is the difference between a Gaussian kernel a polynomial kernel a linear kernel and an RBF based kernel,917
1381,3920,"Z Score is free of any scale, hence it is used as a transformation technique while we need to make any variable unit free in various statistical techniques. Also, it is used to identifying outliers in a univarite way.  Z-test is a statistical technique to test the Null Hypothesis against the Alternate Hypothesis.",How is the Z test different from Z score analysis,3920
1382,2626,"To help you get started in the field, we've assembled a list of the best Big Data courses available.Simplilearn. Simplilearn's Big Data Course catalogue is known for their large number of courses, in subjects as varied as Hadoop, SAS, Apache Spark, and R.  Cloudera.  Big Data University.  Hortonworks.  Coursera.",How do you learn big data,2626
1383,5818,"An -dimensional vector, i.e., a vector ( , , , ) with components. In dimensions greater than or equal to two, vectors are sometimes considered synonymous with points and so n-tuples ( , , , ) are sometimes called points in n-space.",What is an N dimensional vector,5818
1384,6363,Stochastic gradient descent (SGD) computes the gradient for each update using a single training data point x_i (chosen at random). The idea is that the gradient calculated this way is a stochastic approximation to the gradient calculated using the entire training data.,What is an intuitive explanation of stochastic gradient descent,6363
1385,6426,a. it allows us to disregard the size of the sample selected when the population is not normal.  it allows us the disregard the shape of the population when n is large.,Why is the central limit theorem so important to the study of sampling distributions,6426
1386,10033,"Whereas multiple regression predicts a single dependent variable from a set of multiple independent variables, canonical correlation simultaneously predicts multiple dependent variables from multiple independent variables.",When would someone use canonical correlation analysis versus multiple multiple regressions,10033
1387,9270,"In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as ""Lemons are sour"", that all humans are expected to know.  Common sense knowledge also helps to solve problems in the face of incomplete information.",What is common sense in artificial intelligence,9270
1388,3213,The sample variance is not always smaller than the population variance.,Is sample variance always smaller than population variance,3213
1389,8766,"At its core, a loss function is incredibly simple: it's a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your loss function will output a higher number. If they're pretty good, it'll output a lower number.",What does the equation for the loss function do conceptually,8766
1390,1367,"OLS does not require that the error term follows a normal distribution to produce unbiased estimates with the minimum variance. However, satisfying this assumption allows you to perform statistical hypothesis testing and generate reliable confidence intervals and prediction intervals.",Does OLS require normal distribution,1367
1391,10161,"The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems.",Can Knn be used for classification,10161
1392,8336,"The central limit theorem states that the sampling distribution of the mean approaches a normal distribution, as the sample size increases.  Therefore, as a sample size increases, the sample mean and standard deviation will be closer in value to the population mean μ and standard deviation σ .",Does the mean increase with sample size,8336
1393,9911,"A parametric model is one where we assume the 'shape' of the data, and therefore only have to estimate the coefficients of the model. A non-parametric model is one where we do not assume the 'shape' of the data, and we have to estimate the most suitable form of the model, along with the coefficients.",What is the difference between parametric and non parametric models,9911
1394,4459,"Model calibration is done by adjusting the selected parameters such as growth rates, loss rates in the model to obtain a best fit between the model calculations and the monthly average field data (Set #1) collected during first year (June 18, 2004–June 27, 2005).",How do you calibrate a model,4459
1395,6494,Class limits are the least and greatest numbers that can belong to the class. Class boundaries are the numbers that separate classes without forming gaps between them.,What is the difference between class limits and class boundaries quizlet,6494
1396,9295,"The term convolution refers to the mathematical combination of two functions to produce a third function. It merges two sets of information. In the case of a CNN, the convolution is performed on the input data with the use of a filter or kernel (these terms are used interchangeably) to then produce a feature map.",What is CNN convolution operation,9295
1397,981,"In this context, a neural network is one of several machine learning algorithms that can help solve classification problems. Its unique strength is its ability to dynamically create complex prediction functions, and emulate human thinking, in a way that no other algorithm can.",Can neural networks be used for classification,981
1398,10707,"The problem is we always prefer an output having highest probability or lowest distance from reference as our answer and while we are dealing with it, KNN will always give same output for a given set of input repeatedly tested. That means it is quit deterministic.",Is K nearest neighbors example of deterministic algorithm,10707
1399,10289,"According to my POV model accuracy is more important and its all depends on the training data.  Model performance can be improved using distributed computing and parallelizing over the scored assets, whereas accuracy has to be carefully built during the model training process.",Which is more important to you model accuracy or model performance,10289
1400,4126,"1a : to divide into parts or shares. b : to divide (a place, such as a country) into two or more territorial units having separate political status. 2 : to separate or divide by a partition (such as a wall) —often used with off. Other Words from partition Synonyms More Example Sentences Learn More about partition.",What does partitioning mean,4126
1401,10364,"In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be ""ON"" (1) or ""OFF"" (0), depending on input.",What is the role of activation function in neural network,10364
1402,10035,"Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. Tim Salimans, Diederik P. Kingma. Download PDF. We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction.",What is called as weight normalization in machine learning,10035
1403,7585,"Normalization is the process of organizing data into a related table; it also eliminates redundancy and increases the integrity which improves performance of the query. To normalize a database, we divide the database into tables and establish relationships between the tables.",What is normalization and its types,7585
1404,2356,Here are 13 ways you can naturally increase your eagerness to learn and keep feeding your curiosity to stay on your learning goals.Just Show Your Eagerness.  Stay Updated.  Don't Stop Developing Your Skills.  Look for Challenges.  Learn Lateral Thinking.  Be Open to New Experiences.  Start to Be Interesting.  Gain Initial Knowledge.More items•,In what ways do I become more eager to learn and improve myself,2356
1405,8582,A z-score measures exactly how many standard deviations above or below the mean a data point is.  A negative z-score says the data point is below average. A z-score close to 0 says the data point is close to average. A data point can be considered unusual if its z-score is above 3 or below −3 .,What is a z score in math,8582
1406,5906,"Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. at a single point in time. Because data points in time series are collected at adjacent time periods there is potential for correlation between observations.",How do you collect time series data,5906
1407,388,"Stochastic vs. In general, stochastic is a synonym for random. For example, a stochastic variable is a random variable. A stochastic process is a random process. Typically, random is used to refer to a lack of dependence between observations in a sequence.",What is the difference between stochastic and random,388
1408,8848,"Entry level positions require at least a bachelor's degree while positions entailing supervision, leadership or administrative roles frequently require master's or doctoral degrees. Typical coursework involves study of: Various level of math, including probability, statistics, algebra, calculus, logic and algorithms.",What should I study to work with artificial intelligence,8848
1409,4788,"To find the harmonic mean of a set of n numbers, add the reciprocals of the numbers in the set, divide the sum by n, then take the reciprocal of the result.",How do you calculate harmonic mean,4788
1410,7757,"For example, a perfect precision and recall score would result in a perfect F-Measure score:F-Measure = (2 * Precision * Recall) / (Precision + Recall)F-Measure = (2 * 1.0 * 1.0) / (1.0 + 1.0)F-Measure = (2 * 1.0) / 2.0.F-Measure = 1.0.",How do you calculate precision and recall,7757
1411,2230,How to Perform Systematic Sampling: StepsStep 1: Assign a number to every element in your population.  Step 2: Decide how large your sample size should be.  Step 3: Divide the population by your sample size.  Step 1: Assign a number to every element in your population.Step 2: Decide how large your sample size should be.More items•,What are the steps of systematic sampling,2230
1412,3630,Using too large a batch size can have a negative effect on the accuracy of your network during training since it reduces the stochasticity of the gradient descent.,How does a larger batch size affect your training accuracy,3630
1413,4851,"Unlike the previous measures of variability, the variance includes all values in the calculation by comparing each value to the mean. To calculate this statistic, you calculate a set of squared differences between the data points and the mean, sum them, and then divide by the number of observations.",How do you compare variability of two data sets,4851
1414,7273,"The median is the middle number in a sorted, ascending or descending, list of numbers and can be more descriptive of that data set than the average.  If there is an odd amount of numbers, the median value is the number that is in the middle, with the same amount of numbers below and above.",What is median value in statistics,7273
1415,5135,"Random errors often have a Gaussian normal distribution (see Fig. 2). In such cases statistical methods may be used to analyze the data. The mean m of a number of measurements of the same quantity is the best estimate of that quantity, and the standard deviation s of the measurements shows the accuracy of the estimate.",Do random errors always have a Gaussian distribution,5135
1416,2557,"Softmax is used for multi-classification in the Logistic Regression model, whereas Sigmoid is used for binary classification in the Logistic Regression model. This is similar to the Sigmoid function. The difference is that, in the denominator, we sum together all of the values.",What is the difference between sigmoid and Softmax,2557
1417,6899,"So while L2 regularization does not perform feature selection the same way as L1 does, it is more useful for feature *interpretation*: a predictive feature will get a non-zero coefficient, which is often not the case with L1.",Can we use l2 regularization for feature selection,6899
1418,1313,"In general, an LSTM can be used for classification or regression; it is essentially just a standard neural network that takes as input, in addition to input from that time step, a hidden state from the previous time step. So, just as a NN can be used for classification or regression, so can an LSTM.",Can Lstm be used for classification,1313
1419,4867,"Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale).",What is multinomial logistic regression used for,4867
1420,8660,5 | Problems and Issues of Linear RegressionSpecification.Proxy Variables and Measurement Error.Selection Bias.Multicollinearity.Autocorrelation.Heteroskedasticity.Simultaneous Equations.Limited Dependent Variables.More items,What are some problems that are encountered when creating a regression model,8660
1421,5915,"Factor Analysis in SPSS To conduct a Factor Analysis, start from the “Analyze” menu.  This dialog allows you to choose a “rotation method” for your factor analysis.  This table shows you the actual factors that were extracted.  E.  Finally, the Rotated Component Matrix shows you the factor loadings for each variable.More items",How do you do factor analysis in SPSS,5915
1422,9691,"where Ua is size m × n, Ub is size m × (m - n), and Σa is of size n × n. Then A = UaΣaVH is called the reduced SVD of the matrix A. In this context the SVD defined in Equation (1) is sometimes referred to as the full SVD for contrast. Notice that Ua is not unitary, but it does have orthogonal columns.",What is reduced SVD,9691
1423,9822,"The data structure which is being used in DFS is stack. The process is similar to BFS algorithm. In DFS, the edges that leads to an unvisited node are called discovery edges while the edges that leads to an already visited node are called block edges.",Which data structure is used by depth first search algorithm,9822
1424,3834,A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data.  Supervised learning uses classification and regression techniques to develop predictive models.,How does supervised learning algorithm work,3834
1425,9340,"How to Use K-means Cluster Algorithms in Predictive AnalysisPick k random items from the dataset and label them as cluster representatives.Associate each remaining item in the dataset with the nearest cluster representative, using a Euclidean distance calculated by a similarity function.Recalculate the new clusters' representatives.More items",How do you predict using clustering,9340
1426,4338,"Hold-out is when you split up your dataset into a 'train' and 'test' set. The training set is what the model is trained on, and the test set is used to see how well that model performs on unseen data.",What is hold out in machine learning,4338
1427,2976,"A random process is a time-varying function that assigns the outcome of a random experiment to each time instant: X(t). • For a fixed (sample path): a random process is a time varying function, e.g., a signal.",What does random processes mean,2976
1428,2899,"Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand.",What is a convenience sample in statistics,2899
1429,767,Computing accuracy for clustering can be done by reordering the rows (or columns) of the confusion matrix so that the sum of the diagonal values is maximal. The linear assignment problem can be solved in O(n3) instead of O(n!). Coclust library provides an implementation of the accuracy for clustering results.,How do you find the accuracy of a clustering algorithm,767
1430,5210,"The purpose of factor analysis is to reduce many individual items into a fewer number of dimensions. Factor analysis can be used to simplify data, such as reducing the number of variables in regression models.",What is the purpose of factor analysis,8409
1431,7439,"The equation of a hyperplane is w · x + b = 0, where w is a vector normal to the hyperplane and b is an offset.",How do you calculate Hyperplane,7439
1432,6914,t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance. Same assumptions hold.,What is the difference between t distribution and F distribution,6914
1433,2781,"To analyze this data follow these steps:Open the file KAPPA.SAV.  Select Analyze/Descriptive Statistics/Crosstabs.Select Rater A as Row, Rater B as Col.Click on the Statistics button, select Kappa and Continue.Click OK to display the results for the Kappa test shown here:",How do I report a kappa statistic,2781
1434,8133,"Decision Tree Splitting Method #1: Reduction in VarianceFor each split, individually calculate the variance of each child node.Calculate the variance of each split as the weighted average variance of child nodes.Select the split with the lowest variance.Perform steps 1-3 until completely homogeneous nodes are achieved.",How do you determine the best split in decision tree,8133
1435,1924,"Bayesian networks encode the dependencies and independencies between variables. Under the causal Markov assumption, each variable in a Bayesian network is independent of its ancestors given the values of its parents.",What is the Markov assumption for a dynamic Bayesian network,1924
1436,208,"value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, the data significantly deviate from a normal distribution. If you need to use skewness and kurtosis values to determine normality, rather the Shapiro-Wilk test, you will find these in our enhanced testing for normality guide.",How do you test for normality,208
1437,3880,"To overcome this prob- lem, the ResNet incorporates skip-connections between layers (He et al., 2016a,b) and the batch-normalization (BN) normalizes the input of activation functions (Ioffe and Szegedy, 2015). These architectures enable an extreme deep neural network to be trained with high performance.",Does ResNet use batch normalization,3880
1438,6273,"Difference Between Temporal and Spatial Databases A spatial database stores and allows queries of data defined by geometric space. Many spatial databases can represent simple coordinates, points, lines and polygons.  A temporal database stores data relating to time whether past, present or future.",What is the difference between spatial temporal data with other type of data,6273
1439,4756,"All Answers (6) Indeed a common rule of thumb is 10 outcome events per predictor, but sometimes this rule is too conservative and can be relaxed (see Vittinghoff E, McCulloch CE. 2007. Relaxing the rule of ten events per variable in logistic and Cox regression.",How many variables should be in a regression model,4756
1440,3171,"How to Calculate a Confusion MatrixStep 1) First, you need to test dataset with its expected outcome values.Step 2) Predict all the rows in the test dataset.Step 3) Calculate the expected predictions and outcomes:",How do you analyze a confusion matrix,3171
1441,9690,"The mean, or average, IQ is 100. Standard deviations, in most cases, are 15 points. The majority of the population, 68.26%, falls within one standard deviation of the mean (IQ 85-115).",What is IQ standard deviation,9690
1442,9959,Advantages of Linear Regression Linear regression has a considerably lower time complexity when compared to some of the other machine learning algorithms. The mathematical equations of Linear regression are also fairly easy to understand and interpret. Hence Linear regression is very easy to master.,What are advantages of different regression algorithms,9959
1443,1297,"Parallel analysis is a method for determining the number of components or factors to retain from pca or factor analysis. Essentially, the program works by creating a random dataset with the same numbers of observations and variables as the original data.",What is parallel analysis in factor analysis,1297
1444,8311,"Chunking in NLP is Changing a perception by moving a “chunk”, or a group of bits of information, in the direction of a Deductive or Inductive conclusion through the use of language.  you will start to get smaller pieces of information about a car.",What is chunking in NLP,8311
1445,5412,The Dirichlet is the multivariate generalization of the beta distribution.  The Dirichlet equals the uniform distribution when all parameters (α1… αk) are equal. The Dirichlet distribution is a conjugate prior to the categorical distribution and multinomial distributions. A compound variant is the Dirichlet-multinomial.,What does Dirichlet mean,5412
1446,1211,"Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks.",Is logistic regression only for binary classification,1211
1447,6250,"Popular ML algorithms include: linear regression, logistic regression, SVMs, nearest neighbor, decision trees, PCA, naive Bayes classifier, and k-means clustering. Classical machine learning algorithms are used for a wide range of applications.",What are the models in machine learning,6250
1448,4616,"A Classification report is used to measure the quality of predictions from a classification algorithm.  The report shows the main classification metrics precision, recall and f1-score on a per-class basis. The metrics are calculated by using true and false positives, true and false negatives.",What is classification report in machine learning,4616
1449,10538,Normality: Data have a normal distribution (or at least is symmetric) Homogeneity of variances: Data from multiple groups have the same variance. Linearity: Data have a linear relationship. Independence: Data are independent.,What are four main assumptions for parametric statistics,10538
1450,5718,"If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound.",What is a good accuracy score in machine learning,5718
1451,2232,0:007:47Suggested clip · 116 seconds[Proof] Sequence is divergent - YouTubeYouTubeStart of suggested clipEnd of suggested clip,How do you prove divergence,2232
1452,3359,"Contrastive Loss: Contrastive refers to the fact that these losses are computed contrasting two or more data points representations. This name is often used for Pairwise Ranking Loss, but I've never seen using it in a setup with triplets. Triplet Loss: Often used as loss name when triplet training pairs are employed.",What is contrastive loss,3359
1453,984,"The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability.  P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1).",How do you find the joint distribution of X and Y,984
1454,2838,"A P value is also affected by sample size and the magnitude of effect. Generally the larger the sample size, the more likely a study will find a significant relationship if one exists. As the sample size increases the impact of random error is reduced.",Why does P value change with sample size,2838
1455,6023,DEFINITION: Primary sampling unit refers to Sampling units that are selected in the first (primary) stage of a multi-stage sample ultimately aimed at selecting individual elements.,What is a primary sampling unit,6023
1456,2442,"Harmonic means are often used in averaging things like rates (e.g., the average travel speed given a duration of several trips). The weighted harmonic mean is used in finance to average multiples like the price-earnings ratio because it gives equal weight to each data point.",When should harmonic mean be used,2442
1457,2227,"The null hypothesis is the one to be tested and the alternative is everything else. In our example, The null hypothesis would be: The mean data scientist salary is 113,000 dollars. While the alternative: The mean data scientist salary is not 113,000 dollars.",What is null and alternative hypothesis example,2227
1458,1728,The independent variable is called the Explanatory variable (or better known as the predictor) - the variable which influences or predicts the values. i.e. if the explanatory variable changes then it affects the response variable. Here Y is the Dependent variable or response variable.,Which variable is the explanatory variable,1728
1459,10849,"Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words.  Tokenization can be done to either separate words or sentences.",Why tokenization is important in NLP,10849
1460,1012,2. What is the area under a conditional Cumulative density function? Explanation: Area under any conditional CDF is 1.,What is the area under the conditional C * * * * * * * * * density function,1012
1461,2497,Each class will have a “lower class limit” and an “upper class limit” which are the lowest and highest numbers in each class. The “class width” is the distance between the lower limits of consecutive classes. The range is the difference between the maximum and minimum data entries.,What is the difference between class interval and class width in statistics,2497
1462,4160,"Accuracy reflects how close a measurement is to a known or accepted value, while precision reflects how reproducible measurements are, even if they are far from the accepted value. Measurements that are both precise and accurate are repeatable and very close to true values.",What is difference between precision and accuracy,4160
1463,627,"You do need distributional assumptions about the response variable in order to make inferences (e.g, confidence intervals), but it is not necessary that the response variable be normallhy distributed.",Does dependent variable need to be normally distributed,627
1464,1689,"Weights are the co-efficients of the equation which you are trying to resolve. Negative weights reduce the value of an output. When a neural network is trained on the training set, it is initialised with a set of weights.  A neuron first computes the weighted sum of the inputs.",How weights are calculated in neural networks,1689
1465,79,Calculating the distance of various points in the scene relative to the position of the camera is one of the important tasks for a computer vision system.,What is depth computer vision,79
1466,1819,"Abstract. This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified. The goal of supervised clustering is to identify class-uniform clusters that have high probability densities.",What is supervised clustering,1819
1467,351,"Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings.",Can random forest handle missing data,351
1468,3899,"A mathematical function with symbol εijk defined to switch between the discrete values of +1, 0, and -1, depending on the values of the three indices i, j, and k: It is one of the tools used in Einstein's summation notation to handle operations equivalent to cross products in vector notation.",What is alternating tensor,3899
1469,7551,"They are continuous vs discrete distributions. A first difference is that multinomial distribution M(N,p) is discrete (it generalises binomial disrtibution) whereas Dirichlet distribution is continuous (it generalizes Beta distribution).",What makes the Dirichlet distribution different from a multinomial distribution,7551
1470,4289,The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them.,Is CNN generative or discriminative,4289
1471,6728,Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).,What is machine translation in AI,6728
1472,6957,"The value of the z-score tells you how many standard deviations you are away from the mean.  A positive z-score indicates the raw score is higher than the mean average. For example, if a z-score is equal to +1, it is 1 standard deviation above the mean. A negative z-score reveals the raw score is below the mean average.",What does the Z score represent,6957
1473,2487,"Deep-learning software by nameSoftwareCreatorInterfacePlaidMLVertex.AI, IntelPython, C++PyTorchAdam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan (Facebook)Python, C++, JuliaApache SINGAApache Software FoundationPython, C++, JavaTensorFlowGoogle BrainPython (Keras), C/C++, Java, Go, JavaScript, R, Julia, Swift18 riviä lisää",Which software is used for deep learning,2487
1474,2129,"Model-based collaborative filtering algorithms provide item recommendation by first developing a model of user ratings. Algorithms in this category take a probabilistic approach and envision the collaborative filtering process as computing the expected value of a user prediction, given his/her ratings on other items.",What is model based collaborative filtering,2129
1475,5009,Any quantity that has both magnitude and direction is called a vector.  The only difference is that tensor is the generalized form of scalars and vectors . Means scalars and vectors are the special cases of tensor quantities. Scalar is a tensor of rank 0 and vector is a tensor of rank 1.,What is difference between tensor and vector,5009
1476,7977,"The tensor of inertia gives us an idea about how the mass is distributed in a rigid body. Analogously, we can define the tensor of inertia about point O, by writing equation(4) in matrix form.  It follows from the definition of the products of inertia, that the tensors of inertia are always symmetric.",What do you mean by inertia tensor,7977
1477,2719,We have compiled a list of best practices and strategies that you can use to improve your TensorFlow Lite model performance.Choose the best model for the task.  Profile your model.  Profile and optimize operators in the graph.  Optimize your model.  Tweak the number of threads.  Eliminate redundant copies.More items,How can I improve my TensorFlow performance,2719
1478,5552,"In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory.",What is meant by the term instance based learning,5552
1479,3275,"Hypothesis Tests with the Repeated-Measures t (cont.) In words, the null hypothesis says that there is no consistent or systematic difference between the two treatment conditions. Note that the null hypothesis does not say that each individual will have a difference score equal to zero.",What is the null hypothesis for a repeated measures test,3275
1480,9383,"The traditional method of training AI models involves setting up servers where models are trained on data, often through the use of a cloud-based computing platform.  Federated learning brings machine learning models to the data source, rather than bringing the data to the model.",What is Federated AI,9383
1481,4646,"Latent semantic indexing (LSI) is a concept used by search engines to discover how a term and content work together to mean the same thing, even if they do not share keywords or synonyms.  Basically, though, you often need specific keywords on your pages to boost your website traffic.",What is latent semantic indexing and where can it be applied,4646
1482,2378,split testing,What does AB testing stand for,2378
1483,2381,"Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems.",What is a neural network in programming,2381
1484,3515,Cross-validation is a technique in which we train our model using the subset of the data-set and then evaluate using the complementary subset of the data-set.  The three steps involved in cross-validation are as follows : Reserve some portion of sample data-set.,What is cross validation set in machine learning,3515
1485,7888,"If the hazard ratio is less than 1, then the predictor is protective (i.e., associated with improved survival) and if the hazard ratio is greater than 1, then the predictor is associated with increased risk (or decreased survival).",How do you interpret the hazard ratio in Cox Regression,7888
1486,6224,"Collaborative filtering (CF) is a technique used by recommender systems.  In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).",What is meant by collaborative filtering,6224
1487,2805,1 Answer. Transfer learning is when a model developed for one task is reused to work on a second task. Fine tuning is one approach to transfer learning.,What is the difference between transfer learning and fine tuning,2805
1488,9990,"Content validity is different from face validity, which refers not to what the test actually measures, but to what it superficially appears to measure.  In clinical settings, content validity refers to the correspondence between test items and the symptom content of a syndrome.",What is the difference between face and content validity,9990
1489,960,"A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous.",What is a random variable What are the various types of random variables,960
1490,8130,"Brownian motion lies in the intersection of several important classes of processes. It is a Gaussian Markov process, it has continuous paths, it is a process with stationary independent increments (a Lévy process), and it is a martingale. Several characterizations are known based on these properties.",Is Brownian motion a Markov process,8130
1491,3249,"The anti-Martingale, or reverse Martingale, system is a trading methodology that involves halving a bet each time there is a trade loss and doubling it each time there is a gain. This technique is the opposite of the Martingale system, whereby a trader (or gambler) doubles down on a losing bet and halves a winning bet.",What is reverse Martingale,3249
1492,2723,"deep learning - a name for an algorithm in machine learning (just like SVM, Regression etc.) transfer learning - as you may know, in order to train a Neural network it might take long time. So, we use a Neural Network that is already trained and in this way we can extract some features of new sample.",What is the difference between deep learning and transfer learning,2723
1493,8858,"Interpreting the ROC curve Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.",How do you read ROC curve results,8858
1494,6835,"Sampling Frame Error: A type of nonsampling error in a survey caused by a sampling frame (i.e., a list) that is not a perfect representation of the population or universe. That is, the sample list might contain respondents who do not meet the definition of the population or universe.",What is sample frame error,6835
1495,9185,The Lorenz Curve is a graph that illustrates the distribution of income in the economy. It suggests that the distribution of income in the United States is unequal.,What is the Lorenz curve and what does it suggest,9185
1496,349,"The reason that SVMs often outperform ANNs in practice is that they deal with the biggest problem with ANNs, SVMs are less prone to overfitting.",What is one reason we might choose to use support vector machine rather than an artificial neural network,349
1497,2148,"In the chapter on Human Development Indicators, there should be a table that includes the Gini coefficient. For example, in the 2004 edition, they are in table number 14. See also the “Get Indicators” portion of their web site, where you can download an Excel table with the Gini index.",Where is the Gini coefficient data,2148
1498,454,"Data labeling, in the context of machine learning, is the process of detecting and tagging data samples. The process can be manual but is usually performed or assisted by software.",What is Labelling in machine learning,454
1499,377,Definition: Distribution means to spread the product throughout the marketplace such that a large number of people can buy it. Distribution involves doing the following things: Tracking the places where the product can be placed such that there is a maximum opportunity to buy it.,What is meant by a distribution,377