Questions stringlengths 4 233 | Answers stringlengths 32 1.6k |
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assumption linear regression assumption violated | four assumption associated linear regression linearity relationship mean linear homoscedasticity variance residual independence observation independent normality fixed normally distributed extreme violation assumption redundant violation assumption result greater bias variance estimate |
collinearity multicollinearity deal | collinearity linear association predictor multicollinearity situation predictor highly linearly problematic undermines statistical significance independent variable necessarily impact accuracy affect variance prediction reduces interpretation independent variable variance inflation factor vif determine multicollinearit... |
drawback linear | couple drawback linear linear hold strong assumption true application assumes linear relationship multivariate normality multicollinearity auto correlation homoscedasticity linear discrete binary outcome vary flexibility linear |
ridge lasso regression difference | regularization method reduce overfitting least square minimizes sum squared residual result bias variance regularization called ridge regression minimizes sum squared residual plus lambda slope squared additional term called ridge regression penalty increase bias making fit worse decrease variance ridge regression pena... |
nearest neighbor | nearest neighbor classification technique sample classified looking nearest classified point hence nearest example above unclassified classified blue outlier overlook class few sample |
mean | elbow method popular method determine optimal essentially plot squared error graph axis squared error axis once graph distortion decline elbow |
naive bayes naive | naive bayes naive hold strong assumption assumed uncorrelated typically never |
vector svm | vector point touch boundary maximum margin |
pruning decision tree | pruning technique machine learning algorithm reduces decision tree removing section branch tree classifying instance |
random forest naive bayes | random forest ensemble learning technique build decision tree random forest involve creating multiple decision tree bootstrapped datasets original randomly selecting subset variable step decision tree selects mode prediction decision tree relying majority win reduces risk error individual tree random forest offer sever... |
random forest v svm | couple reason random forest choice algorithm vector machine random forest allow determine feature importance svm random forest quicker simpler build svm multi classification problem svms require v rest method le scalable memory intensive |
decision tree | asking question random forest decision tree answer random forest ensemble method take weak decision tree strong learner random forest accurate robust le prone overfitting |
difference adaboosted tree gradient boosted tree | adaboost boosted algorithm similar random forest couple significant difference rather forest tree adaboost typically make forest stump stump tree node leaf stump decision weighted equally final decision stump le error accuracy higher stump created important subsequent stump emphasizes importance sample incorrectly clas... |
bias variance tradeoff | bias estimator difference expected true bias tends oversimplified underfitting variance represents sensitivity noise variance overfitting therefore bias variance tradeoff machine learning model lower variance higher bias vice versa generally optimal balance error minimized |
explain bootstrap sampling method give example | technically speaking bootstrap sampling method resampling method us random sampling replacement essential random forest algorithm ensemble learning algorithm |
difference bagging boosting | bagging known bootstrap aggregating multiple model learning algorithm trained bootstrapped sample original dataset random forest example above vote taken model output boosting variation bagging individual built sequentially iterating specifically point falsely classified emphasized done improve overall accuracy once bu... |
xgboost handle bias variance tradeoff | xgboost ensemble machine learning algorithm leverage gradient boosting algorithm essence xgboost bagging boosting technique steroid therefore xgboost handle bias variance similar boosting technique boosting ensemble meta algorithm reduces bias variance take weighted average weak model focusing weak prediction iterating... |
cross validation | cross validation essentially technique ass performs independent dataset simplest example cross validation split group validation testing build validation tune hyperparameters testing evaluate final |
difference batch learning | batch learning known offline learning group pattern learning familiar dataset build whole dataset once learning hand approach ingests observation learning efficient longer once consumed technically mean don |
give several way deal missing value | way handle null value omit row null value altogether replace null value measure central tendency mean median mode replace eg none predict null value variable example row null weight height replace null average weight given height lastly leave null value machine learning automatically deal null value |
mean imputation missing acceptable practice | mean imputation practice replacing null value mean mean imputation generally bad practice doesn feature correlation example imagine showing age fitness score imagine eighty missing fitness score took average fitness score age range eighty appear higher fitness score actually mean imputation reduces variance increase bi... |
confusion matrix | confusion matrix known error matrix summarized ass performance classification correct incorrect prediction summarized count value broken |
supervised v unsupervised learning | supervised learning involves learning labeled dataset target variable known unsupervised learning draw inference pattern input reference labeled outcome target variable |
ensemble learning | ensemble learning method multiple learning algorithm conjunction purpose doing allows achieve higher predictive performance individual algorithm itself example random forest |
identify outlier | couple way identify outlier z score standard deviation lie standard deviation calculate standard deviation multiply identify point outside range likewise calculate z score given equal outlier few contingency considered method normally distributed applicable set presence outlier throw z score interquartile range iqr iqr... |
inlier | inlier observation lie rest dataset unusual error lie dataset typically harder identify outlier requires external identify |
outlier treated | couple way remove outlier garbage try example linear might treat outlier differently linear normalize narrow range algorithm outlier random forest |
collaborative filtering filtering similar | filtering property object similar example filtering recommender recommend genre directed director collaborative filtering behavior compared user user similar behavior dictate recommended give simple example bought bought recliner recommended recliner |
principal component analysis explain sort problem pca | simplest sense pca involves higher dimensional eg dimension smaller space eg dimension lower dimension dimension instead dimension keeping original variable pca commonly compression purpose reduce memory speed algorithm visualization purpose making easier summarize |
difference validation test | generally validation tune hyperparameters testing evaluate final |
avoid overfitting | don overfitting modeling error function fit closely resulting level error introduced way prevent overfitting cross validation cross validation technique ass performs independent dataset simplest example cross validation split group testing build testing test regularization overfitting occurs model higher degree polynom... |
step wrangling cleaning applying machine learning algorithm | step taken wrangling cleaning common step listed below profiling almost everyone start getting understanding dataset specifically shape dataset shape numerical variable describe visualization sometimes useful visualize histogram boxplots scatterplots understand relationship variable identify potential outlier syntax er... |
deal unbalanced binary classification | way handle unbalanced binary classification assuming identify minority reconsider metric evaluate accuracy might metric ll example explain let bank withdrawal fraudulent withdrawal simply classified instance fraudulent accuracy therefore consider metric precision recall method improve unbalanced binary classification i... |
difference precision recall | recall attempt answer proportion actual positive identified correctly precision attempt answer proportion positive identification actually correct |
mean square error bad measure performance suggest instead | mean squared error mse give relatively weight error therefore mse tends put emphasis deviation robust alternative mae mean absolute deviation |
explain false positive false negative important example false positive important false negative false negative important false positive | false positive incorrect identification presence condition absent false negative incorrect identification absence condition actually present example false negative important false positive screening cancer worse someone doesn cancer instead saying someone later realizing don subjective argument false positive worse fal... |
feature selection method variable | type method feature selection filter method wrapper method filter method linear discrimination analysis anova chi square wrapper method forward selection test feature keep adding until fit backward selection test removing work |
briefly explain basic neural work | core neural essentially mathematical equation take input variable equation output variable neural input layer hidden layer output layer input layer consists feature variable input variable independent variable denoted xn hidden layer consists hidden node hidden unit similarly output variable consists output unit beginn... |
rectified linear unit activation function | rectified linear unit known relu function known activation function sigmoid function tanh function performs gradient descent faster |
weight initialized | weight neural initialized randomly expectation stochastic gradient descent initialized weight zero hidden unit exactly signal example weight initialized hidden unit zero signal |
happens learning | learning train slowly minimal update weight iteration thus update reaching minimum learning cause undesirable divergent behavior loss function due drastic update weight fail converge |
recurrent neural network | recurrent neural network known rnns neural network allow output input having hidden commonly recognize pattern sequence etc |
role activation function | purpose activation function introduce linearity output neuron activation function decides whether neuron activated calculating weighted sum further adding bias |
defined | probability obtaining observed test assuming null hypothesis correct smaller mean stronger evidence favor alternative hypothesis |
covariance correlation | covariance quantitative measure extent deviation variable mean match deviation mean correlation measurement relationship variable covariance variable normalized variance variable |
number | number theory trial increase average result become closer expected eg flipping head fair coin closer |
central limit theorem explain important | central limit theorem sampling distribution sample mean approach normal distribution sample get larger matter shape population distribution central limit theorem important hypothesis testing calculate confidence interval |
markov | modeling stochastic agent make random decision assumption referred markov |
statistical | statistical refers binary hypothesis probability test reject null hypothesis given alternative hypothesis true |
confounding variable | confounding variable confounder variable influence dependent variable independent variable causing spurious association mathematical relationship variable associated causally |
experimental contrast observational | observational come observational study observe certain variable intervening try determine correlation experimental come experimental study intervention certain variable hold constant determine causality |
explain selection bias regard dataset variable selection important procedure missing handling worse | selection bias phenomenon selecting individual group analysis proper randomization achieved ultimately resulting sample representative population understanding identifying selection bias important significantly skew false insight particular population type selection bias sampling bias biased sample caused random sampli... |
difference interpolation extrapolation matter | interpolation prediction input lie observed value extrapolation prediction input outside observed value important distinction interpolation generally accurate extrapolation |
give example median measure mean | outlier positively negatively skew |
survivorship bias | phenomenon survived included excluded analysis thus creating biased sample example sreenivasan chandrasekar enroll gym membership attend few face fit motivated exercising everyday whenever gym few become depressed aren able stick schedule motivation saw gym didn enrolled gym membership stopped turning gym didn |
root cause analysis identify cause v correlation give example | root cause analysis method problem solving identifying root cause problem identify correlation simple analysis identify causation conducting experiment variable isolated ideally |
give type statistical bias explain example | sampling bias refers biased sample caused random sampling give example imagine ask prefer grape banana surveyed female concluded majority grape demonstrated sampling bias confirmation bias tendency favour confirms belief survivorship bias phenomenon survived included excluded analysis thus creating biased sample |
explain tailed distribution example relevant phenomenon tail important classification regression problem | tailed distribution heavy tailed distribution tail tail drop gradually asymptotically practical example pareto principle commonly known rule selling v others important mindful tailed distribution classification regression problem least frequently occurring value majority population ultimately deal outlier conflict mach... |
testing practice | testing statistical hypothesis testing meant randomized experiment variable commonly marketing |
bias | thing minimize bias common thing randomization participant assigned chance random sampling sampling equal probability chosen |
give example gaussian distribution nor log normal | categorical won gaussian distribution lognormal distribution exponential distribution eg amount battery last amount until earthquake occurs |
ass statistical significance insight | perform hypothesis testing determine statistical significance null hypothesis alternative hypothesis calculate probability obtaining observed test assuming null hypothesis true significance alpha le alpha reject null word result statistically significant |
homicide scotland fell reported noteworthy | poisson distribution question mean lambda variance mean standard deviation square root mean confidence interval implies z score standard deviation sqrt therefore confidence interval confidence interval assume noteworthy |
difference boxplot histogram | boxplots histogram visualization distribution communicate differently histogram bar chart frequency numerical variable value approximate probability distribution given variable allows quickly understand shape distribution variation potential outlier boxplots communicate aspect distribution shape distribution plot gathe... |
meaning acf pacf | understand acf pacf autocorrelation serial correlation autocorrelation look degree similarity given lagged itself therefore autocorrelation function acf tool pattern specifically correlation point separated various lag example acf mean point perfectly correlated themselves acf mean correlation pacf short partial autoco... |
experiment feature thinking metric matter | conduct test determine introduction feature statistically significant improvement given metric metric chosen depends goal feature example feature introduced increase conversion rate traffic retention rate formulate null hypothesis feature improve metric alternative hypothesis feature improve metric create test random s... |
prove male average taller female knowing gender height | hypothesis testing prove male taller average female null hypothesis male female height average alternative hypothesis average height male greater average height female collect random sample height male female test determine reject null |
say double ad newsfeed figure idea | perform test splitting user group normal ad test double ad choose metric define idea example null hypothesis doubling ad reduce spent facebook alternative hypothesis doubling ad won impact spent facebook choose metric active user churn conduct test determine statistical significance test reject reject null |
tell given coin biased | isn trick question answer simply perform hypothesis test null hypothesis coin biased probability flipping head equal alternative hypothesis coin biased flip coin calculate z score sample le calculate statistic against alpha tailed test alpha null rejected coin biased alpha null rejected coin biased |
define metric | isn fit metric metric chosen evaluate machine learning depends various factor regression classification task objective eg precision v recall distribution target variable metric adjusted squared mae mse accuracy recall precision score go |
dimension reduction important | dimensionality reduction reducing dataset important mainly reduce variance overfitting wikipedia four advantage dimensionality reduction reduces storage space removal multi collinearity improves interpretation parameter machine learning becomes easier visualize reduced dimension avoids curse dimensionality |
naive bayes bad improve spam detection algorithm us naive bayes | major drawback naive bayes hold strong assumption assumed uncorrelated typically never improve algorithm us naive bayes decorrelating assumption hold true |
regression fit | couple metric squared adjusted squared relative measure fit explained answer score evaluates null hypothesis regression coefficient equal zero v alternative hypothesis least doesn equal zero rmse absolute measure fit |
decision tree | decision tree popular operation strategic planning machine learning square above called node node accurate decision tree generally node decision tree decision called leaf tree decision tree intuitive easy build fall short come accuracy |
kernel explain kernel trick | kernel computing dot vector possibly dimensional feature space kernel function sometimes called generalized dot kernel trick method linear classifier solve linear problem transforming linearly inseparable linearly separable one higher dimension |
beneficial perform dimensionality reduction fitting svm | greater observation performing dimensionality reduction generally improve svm |
overfitting | overfitting error fit resulting variance bias consequence overfit inaccurately predict point though accuracy |
boosting | boosting ensemble method improve reducing bias variance ultimately converting weak learner strong learner idea train weak learner sequentially iterate improve learning learner |
difference convex convex function mean function convex | convex function drawn point graph lie above graph minimum convex function drawn point graph intersect point graph characterized wavy function convex mean likelihood function minimum instead global minimum typically undesired machine learning model optimization perspective |
outlier explain might screen outlier dataset explain inlier might screen dataset | outlier differs significantly observation depending cause outlier bad machine learning perspective worsen accuracy outlier caused measurement error important remove dataset couple way identify outlier z score standard deviation lie standard deviation calculate standard deviation multiply identify point outside range li... |
handle missing imputation technique recommend | several way handle missing delete row missing mean median mode imputation assigning unique predicting missing value algorithm support missing value random forest method delete row missing ensures bias variance added removed ultimately robust accurate recommended lot percentage missing value |
compiling uploaded month notice spike uploads particular spike picture uploads might cause test | potential reason spike uploads feature implemented involves uploading gained lot traction user example feature give ability create album similarly possible uploading intuitive improved month viral social movement involved uploading lasted eg movember something scalable possible spike due posting themselves costume hall... |
calculate needed sample | margin error formula determine desired sample |
running pollster polled hundred sixty claimed vote relax | assume opponent assume confidence interval give z score hat z give confidence interval therefore given confidence interval okay worst scenario tying relax otherwise relax until got claim |
given train having column million row classification problem manager asked reduce dimension computation reduced machine memory constraint practical assumption | lower ram close application machine browser memory put randomly sample mean create smaller let having variable row computation reduce dimensionality separate numerical categorical variable remove correlated variable numerical variable ll correlation categorical variable ll chi square test pca pick component explain max... |
rotation necessary pca happen don rotate component | rotation orthogonal necessary maximizes difference variance captured component make component easier interpret forget motive doing pca aim fewer component explain maximum variance doing rotation relative component doesn change actual coordinate point don rotate component effect pca diminish ll component explain varianc... |
given missing value spread along standard deviation median percentage remain unaffected | question enough hint thinking spread across median let assume normal distribution normal distribution lie standard deviation mean mode median leaf unaffected therefore remain unaffected missing value |
given cancer detection ve build classification achieved accuracy shouldn happy performance | worked enough set deduce cancer detection imbalanced imbalanced accuracy measure performance given might predicting majority correctly interest minority actually got diagnosed cancer hence evaluate performance sensitivity true positive specificity true negative measure determine wise performance classifier minority per... |
naive bayes naive | naive bayes naive assumes equally important independent assumption rarely true scenario |
explain prior probability likelihood marginal likelihood context naive bayes algorithm | prior probability nothing proportion dependent binary variable closest guess further example dependent variable binary proportion spam spam hence estimate chance classified spam likelihood probability classifying given observation presence variable example probability word spam likelihood marginal likelihood probabilit... |
working manager asked build accuracy decision tree algorithm work fairly kind later tried regression got higher accuracy decision tree happen | known posse linearity hand decision tree algorithm known detect linear interaction reason decision tree failed robust prediction couldn linear relationship regression therefore learned linear regression robust prediction given satisfies linearity assumption |
assigned involves helping delivery problem delivery aren able deliver result customer unhappy keep happy delivering machine learning algorithm | might started hopping ml algorithm mind wait asked test machine learning fundamental machine learning problem route optimization problem machine learning problem consist thing exist pattern solve mathematically writing exponential equation always factor decide machine learning tool solve particular problem |
came suffering bias variance algorithm tackle | bias occurs predicted value near actual value word becomes flexible enough mimic distribution sound achievement forget flexible generalization capability mean tested unseen give disappointing situation bagging algorithm random forest tackle variance problem bagging algorithm divide subset repeated randomized sampling s... |
given contains variable highly correlated manager asked run pca remove correlated variable | chance might tempted incorrect discarding correlated variable substantial effect pca presence correlated variable variance explained particular component get inflated example variable correlated run pca principal component exhibit twice variance exhibit uncorrelated variable adding correlated variable let pca put impor... |
spending several anxious build accuracy result build gbm model thinking boosting algorithm magic unfortunately neither model perform benchmark score finally decided combine model though ensembled model known accuracy unfortunate miss | ensemble learner idea combining weak learner create strong learner learner superior result combined model uncorrelated gbm model got accuracy improvement suggests model correlated problem correlated model model example classified chance done actual therefore ensemble learner built premise combining weak uncorrelated mo... |
knn kmeans clustering | don mislead name fundamental difference algorithm kmeans unsupervised nature knn supervised nature kmeans clustering algorithm knn classification regression algorithm kmeans algorithm partition cluster cluster formed homogeneous point cluster close algorithm try maintain enough separability cluster due unsupervised nat... |
true positive recall write equation | true positive recall equal having formula tp tp fn |
built multiple regression isn wanted improvement remove intercept term becomes possible | possible understand significance intercept term regression intercept term show prediction independent variable mean prediction formula ymean predicted intercept term present evaluates wrt mean absence intercept term ymean evaluation denominator equation becomes smaller actual resulting higher |
analyzing manager informed regression suffering multicollinearity true losing build | multicollinearity create correlation matrix identify remove variable having correlation above deciding threshold subjective addition calculate vif variance inflation factor presence multicollinearity vif implies serious multicollinearity tolerance indicator multicollinearity removing correlated variable might lead loss... |
ridge regression favorable lasso regression | conceptually lasso regression variable selection parameter shrinkage whereas ridge regression parameter shrinkage coefficient presence correlated variable ridge regression might preferred choice ridge regression work situation least square estimate higher variance therefore depends objective |
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