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What is the Mean Median Mode standard deviation for the sample and population? | Mean It is an important technique in statistics. Arithmetic Mean can also be called an average. It is the
number of the quantity obtained by summing two or more numbers/variables and then dividing the sum
by the number of numbers/variables.
Mode The mode is also one of the types for finding the average. A mode is a ... |
Why deep learning is better than machine learning? | Though traditional ML algorithms solve a lot of our cases, they are not
useful while working with high dimensional data that is where we have a
large number of inputs and outputs. For example, in the case of
handwriting recognition, we have a large amount of input where we will
have different types of inputs associ... |
What is the Random Forest Algorithm? | Random Forest is an ensemble machine learning algorithm that follows the bagging technique. The
base estimators in the random forest are decision trees. Random forest randomly selects a set of
features that are used to decide the best split at each node of the decision tree.
Looking at it step-by-step, this is what ... |
How to evaluate that data does not have any outliers? | In statistics, outliers are data points that don’t belong to a certain population. It is an abnormal observation
that lies far away from other values. An outlier is an observation that diverges from otherwise well
structured data.
Detection:
Method 1 — Standard Deviation: In statistics, If a data distribution is app... |
What is Principal Component Analysis(PCA)? | The main idea of principal component analysis (PCA) is to reduce the dimensionality of a data set
consisting of many variables correlated with each other, either heavily or lightly, while retaining the
variation present in the dataset, up to the maximum extent. The same is done by transforming the
variables to a new... |
What is backward propagation? | Back-propagation is the essence of the neural net training and this
method of fine-tuning the weights of a neural net based on the errors rate
obtained in the previous epoch. Proper tuning of the weights allows us to
reduce error rates and to make the model reliable by increasing its
generalisation.
Backpropagatio... |
what is RandomizedSearchCV? | Randomized search CV is used to perform a random search on hyperparameters. Randomized
search CV uses a fit and score method, predict proba, decision_func, transform, etc..,
The parameters of the estimator used to apply these methods are optimized by cross-validated
search over parameter settings.
In contrast to Gr... |
Difference between logistic and linear regression? | Linear regression models data using continuous numeric value. As against, logistic regression models
the data in the binary values.
Linear regression requires to establish the linear relationship among dependent and independent
variables, whereas it is not necessary for logistic regression.
In linear regression, th... |
What are the encoding techniques you have applied with Examples ? | In many practical data science activities, the data set will contain categorical variables. These variables
are typically stored as text values". Since machine learning is based on mathematical equations, it would
cause a problem when we keep categorical variables as is.
Let's consider the following dataset of fruit... |
what is annova test? | -Anova test:-
ANOVA, also called an analysis of variance, is used to compare multiples
(three or more) samples with a single test.
Useful when there are more than three populations. Anova compares the
variance within and between the groups of the population. If the variation is
much larger than the within variation... |
What is the statistical test for data validation with an example, Chi-square | Before discussing the different statistical test, we need to get a clear
understanding of what a null hypothesis is. A null hypothesis proposes that
has no significant difference exists in the set of a given observation.
Null: Two samples mean are equal. Alternate: Two samples mean are not
equal.
For rejecting th... |
what is pruning | There are two types of pruning: Pre-pruning and Post-pruning.
1. Pre-pruning is also known as the early stopping criteria. As the name suggests, the criteria
are set as parameter values while building the model. The tree stops growing when it meets
any of these pre-pruning criteria, or it discovers the pure classes.... |
what is Z test | In a z-test, the samples are assumed to be normal distributed. A z score is
calculated with population parameters as “population mean” and
“population standard deviation” and it is used to validate a hypothesis that
the sample drawn belongs to the same population.
The statistics used for this hypothesis testing is ... |
What is market basket analysis | Market basket analysis is the study of items that are purchased or grouped in a single transaction or
multiple, sequential transactions. Understanding the relationships and the strength of those relationships
is valuable information that can be used to make recommendations, cross-sell, up-sell, offer coupons, etc.
M... |
What is the difference between AI, Data Science, ML, and DL? | Artificial Intelligence (AI) is purely math and scientific exercise, but when it became computational, it
started to solve human problems formalized into a subset of computer science. Artificial intelligence has
changed the original computational statistics paradigm to the modern idea that machines co... |
What is machine learning? | Machine learning is a branch of artificial intelligence where systems learn patterns from data to make predictions or decisions. |
What are F1 Score, precision and recall? | Recall:-
Recall can be defined as the ratio of the total number of correctly classified positive examples
divide to the total number of positive examples.
1. High Recall indicates the class is correctly recognized (small number of FN).
2. Low Recall indicates the class is incorrectly recognized (large number of FN).... |
What is GridSearchCV? | Grid search is the process of performing hyperparameter tuning to determine the optimal values for a given model. |
what is T test | t-test used to compare the mean of the given samples. Like z-test, t-test
also assumed a normal distribution of the samples. A t-test is used when
the population parameters (mean and standard deviation) are unknown.
There are three versions of t-test
1. Independent samples t-test which compare means for two grou... |
What is the Confusion Matrix? | Answer:
A confusion matrix is a table that is often used to describe the performance of a classification model
(or “classifier”) on a set of test data for which the true values are known. It allows the visualization
of the performance of an algorithm.
A confusion matrix is a summary of prediction results on a clas... |
What is t-SNE? | (t-SNE) t-Distributed Stochastic Neighbor Embedding is a non-linear dimensionality reduction algorithm
used for exploring high-dimensional data. It maps multi-dimensional data to two or more dimensions
suitable for human observation. With the help of the t-SNE algorithms, you may have to plot fewer
exploratory data ... |
What is Variance and Bias tradeoff? | Answer:
In predicting models, the prediction error is composed of two different errors
1. Bias
2. Variance
P a ge 9 | 22
It is important to understand the variance and bias trade-off which tells about to minimize the Bias
and Variance in the prediction and avoids overfitting & under fitting of the model.
Bias: I... |
what is True Positive Rate & True Negative Rate | True Positive Rate:
Sensitivity (SN) is calculated as the number of correct positive predictions divided by the
total number of positives. It is also called Recall (REC) or true positive rate (TPR). The best
sensitivity is 1.0, whereas the worst is 0.0.
True Negative Rate
Specificity (SP) is calculated as the numbe... |
How to initialise biases in deep learning? | It is possible and common to initialise the biases to be zero since the
random numbers in the weights provide the asymmetry braking. For ReLU
non-linearities, some people like to use small constant value such as 0.01
for all biases because this ensures that all ReLU units fire in the beginning,
therefore obtain, an... |
What is underfitting? | Underfitting occurs when a model is too simple to learn the important patterns in the training data. |
What kind of problem can be solved by using deep learning? | Deep Learning is a branch of Machine Learning, which is used to solve
problems in a way that mimics the human way of solving problems.
Examples:
Image recognition
Object Detection
Natural Language processing- Translation, Sentence formations, text
to speech, speech to text
understand the semantics of actions |
What is overfitting? | Overfitting occurs when a machine learning model learns the training data too closely and performs poorly on new unseen data. |
What is Accuracy and Misclassification Rate? | Accuracy
Accuracy is defined as the ratio of the sum of True Positive and True
Negative by Total(TP+TN+FP+FN)
Misclassification Rate
Misclassification Rate is defined as the ratio of the sum of False Positive and False
Negative by Total(TP+TN+FP+FN)
Misclassification Rate is also called Error Rate. |
How to control leaf height? | To control the leaf size, we can set the parameters:-
1. Maximum depth :
Maximum tree depth is a limit to stop the further splitting of nodes when the specified tree depth has
been reached during the building of the initial decision tree.
NEVER use maximum depth to limit the further splitting of nodes. In other words... |
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