[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-391810-105":59,"doc-detail-391810-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-interview-cheat-sheets","Machine Learning Interview Cheat sheets","","Machine Learning Interview Cheat sheets is a practice-focused reference for machine learning and data science interview topics. It covers core foundations such as bias-variance tradeoff, imbalanced classification, principal component analysis, and Bayes’ theorem/classifiers, along with regression analysis and regularization. It also includes convolutional neural network fundamentals, notable CNN architectures, and ensemble methods. The guide further addresses behavioral interview preparation, including how to prepare and how to answer behavioral questions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-interview-cheat-sheets/391810/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-interview-cheat-sheets/391810.png","ImageObject",300,407,{"name":92,"@type":93},"supergirl","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-25",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the bias-variance tradeoff describe in model performance?","Question",{"text":113,"@type":114},"Bias reflects error between the average model prediction and the ground truth, while variance reflects how much predictions vary with the dataset. Increasing one typically reduces the other, and the goal is to reduce total error through a compromise.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why can accuracy be misleading for imbalanced classification?",{"text":118,"@type":114},"Accuracy can look high even when a model predicts mostly one class. The document emphasizes using precision, recall, specificity, and F1 score to better evaluate performance per class/label.",{"name":120,"@type":111,"acceptedAnswer":121},"What are the main steps of PCA for dimensionality reduction?",{"text":122,"@type":114},"PCA standardizes datapoints, computes the covariance matrix, performs eigen-value decomposition, and sorts eigenvalues/eigenvectors, keeping the top m features to reduce dimensions while preserving information.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},391810,1790436955,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962088121634,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Machine Learning Interview Cheat sheets  \nAqeel Anwar  \nLast Updated: March 2021  \nThis document contains cheat sheets on various topics asked during a Machine Learning/Data science interview. This document is constantly updated to include more topics.  \nClick here to get the updated version  \nTable of Contents  \nBasics of Machine Learning ................................................. 2  \n1. Bias-Variance Trade-o􀀋 ........................................................... 2  \n2. Imbalanced Data in Classi􀀌cation ................................................. 3  \n3. Principal Component Analysis .................................................... 4  \n4. Bayes' Theorem and Classi􀀌er .....................................................5  \n5. Regression Analysis ............................................................... 6  \n6. Regularization in ML ............................................................. 7  \n7. Convolutional Neural Network .................................................... 8  \n8. Famous CNNs .................................................................... 9  \n9. Ensemble Methods in Machine Learning .......................................... 10  \nBehavioral Interview ....................................................... 11  \n1. How to prepare for behavioral interview? ......................................... 11  \n2. How to answer a behavioral question?............................................. 12  \n\n| Cheat Sheet – Bias-Variance Tradeoff |  |  |  |\n| --- | --- | --- | --- |\n| \u003Cbr>What is Bias?\u003Cbr>• Error between average model prediction and ground truth\u003Cbr>\u003Cbr>• The bias of the estimated function tells us the capacity of the underlying model to predict the values\u003Cbr>What is Variance?\u003Cbr>• Average variability in the model prediction for the given dataset\u003Cbr>|  |  |  |\n| • The variance of the estimated function tells you how much the function can adjust to the change in the dataset |  |  |  |\n| High Bias\u003Cbr> Overly-simplified Model\u003Cbr> Under-fitting\u003Cbr> High error on both test and train data |  |  |  |\n| High Variance  Overly-complex Model\u003Cbr> Over-fitting\u003Cbr> Low error on train data and high on test  Starts modelling the noise in the input |  |  |  |\n| \u003C$􀀛 Y zs􀂄$st{􀂂 NBb zs􀂄$st{􀂂 | NBb $sR \u003C$􀀛Y $sR\u003Cbr>| \u003C$􀀛Y $sR NBb zs􀂄$st{􀂂 | NBb $sR \u003C$􀀛Y zs􀂄$st{􀂂 |\n|  |  | \u003Cbr>Minimum Error\u003Cbr>$sR zs􀂄$st{􀂂\u003Cbr>e􀂄􀂄B􀂄\u003Cbr>\u003Cbr>=tI􀂂􀂄TP$􀀜􀀜$t􀀛 FZR􀀜 i$􀀛Y􀀜 !%􀂂􀂄TP$􀀜􀀜$t􀀛 |  |\n| Bias variance Trade-off |  | _􀂄􀂂P􀂂􀂄􀂄􀂂I $P R$9􀂂 BP Is􀀜sR􀂂􀀜 $R R8sjj | _􀂄􀂂P􀂂􀂄􀂄􀂂I $P R$9􀂂 BP Is􀀜sR􀂂􀀜 $R js􀂄􀀛􀂂 |\n|  |  |  |  |\n| • Increasing bias reduces variance and vice-versa\u003Cbr>• Error = bias2 + variance +irreducible error\u003Cbr>• The best model is where the error is reduced.\u003Cbr>• Compromise between bias and variance |  |  |  |\n|  Source: [https://www.cheatsheets.aqeel-anwar.com](https://www.cheatsheets.aqeel-anwar.com)   |  |  |  |\n\n| Cheat Sheet – Imbalanced Data in Classification |  |  |  |\n| --- | --- | --- | --- |\n| \u003Cbr>Blue: Label 1\u003Cbr>\u003Cbr>Green: Label 0\u003Cbr>\u003Cbr>\u003Cbr>Accuracy = ~~ ~~CorTorteacltPredictionPredictionss~~ ~~\u003Cbr>\u003Cbr>\u003Cbr>|  |  |  |\n| Classifier that always predicts label blue yields prediction accuracy of 90% |  |  |  |\n| Accuracy doesn’t always give the correct insight about your trained model |  |  |  |\n| Accuracy: %age correct prediction Correct prediction over total predictions One value for entire network\u003Cbr>Precision: Exactness of model From the detected cats, how many were Each class/label has a value\u003Cbr>actually cats\u003Cbr>Recall: Completeness of model Correctly detected cats over total cats Each class/label has a value F1 Score: Combines Precision/Recall Harmonic mean of Precision and Recall Each class/label has a value |  |  |  |\n| Performance metrics associated with Class 1 |  |  |  |\n| \u003Cbr>\u003Cbr>(Is your prediction correct?) (What did you predict)\u003Cbr> True  Negative \u003Cbr>(Your prediction is correct) (You predicted 0)\u003Cbr>Precision =\u003Cbr>\u003Cbr>F1 score = 2x ~~ ~~((PrecPrec~~ ~~~~ ~~RecRec))\u003Cbr>Specificity =\u003Cbr>\u003Cbr>False +ve r","cbCaijbu88t61Uvj","https://ap.wps.com/l/cbCaijbu88t61Uvj","pdf",6485635,14,"English","# Basics of Machine Learning\n## Bias-Variance Tradeoff\n## Imbalanced Data in Classification\n## Principal Component Analysis\n## Bayes' Theorem and Classifier\n## Regression Analysis\n## Regularization in ML\n## Convolutional Neural Network\n## Famous CNNs\n## Ensemble Methods in Machine Learning\n# Behavioral Interview\n## How to prepare for behavioral interview?\n## How to answer a behavioral question?","[{\"question\":\"What does the bias-variance tradeoff describe in model performance?\",\"answer\":\"Bias reflects error between the average model prediction and the ground truth, while variance reflects how much predictions vary with the dataset. Increasing one typically reduces the other, and the goal is to reduce total error through a compromise.\"},{\"question\":\"Why can accuracy be misleading for imbalanced classification?\",\"answer\":\"Accuracy can look high even when a model predicts mostly one class. The document emphasizes using precision, recall, specificity, and F1 score to better evaluate performance per class/label.\"},{\"question\":\"What are the main steps of PCA for dimensionality reduction?\",\"answer\":\"PCA standardizes datapoints, computes the covariance matrix, performs eigen-value decomposition, and sorts eigenvalues/eigenvectors, keeping the top m features to reduce dimensions while preserving information.\"}]","Machine Learning Interview Cheat sheets | PDF",1790358866,35]