[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-573206-105":3,"detail-sidebar-cat-1-en-105":74,"doc-detail-573206-en":120},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":67,"head_meta":69,"extra_data":71,"updated_unix":73},105,"en","machine-learning-algorithm-cheat-sheet-general-guide-for-choosing-algorithms","Machine Learning Algorithm Cheat Sheet - general guide for choosing algorithms","","Machine learning algorithm cheat sheet for selecting suitable predictive analytics methods based on data nature and the desired outcome. Covers core areas including text analytics with preprocessing, n-gram feature extraction, feature hashing, and Word2Vec, then explains multiclass and two-class classification approaches, regression forecasting techniques, recommenders such as SVD and collaborative filtering, and clustering for grouping similar data. Also summarizes anomaly detection and image classification with practical model options and typical performance traits.",{"@graph":14,"@context":66},[15,34,49],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/posters/","Posters",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/machine-learning-algorithm-cheat-sheet-general-guide-for-choosing-algorithms/573206/",4,{"url":32,"name":10,"@type":35,"author":36,"headline":10,"publisher":39,"fileFormat":42,"inLanguage":8,"description":12,"dateModified":43,"datePublished":43,"encodingFormat":42,"isAccessibleForFree":44,"interactionStatistic":45},"DigitalDocument",{"name":37,"@type":38},"Patrick","Person",{"url":19,"name":40,"@type":41},"DocShare","Organization","application/pdf","2026-10-13",true,{"@type":46,"interactionType":47,"userInteractionCount":4},"InteractionCounter",{"@type":48},"ViewAction",{"@type":50,"mainEntity":51},"FAQPage",[52,58,62],{"name":53,"@type":54,"acceptedAnswer":55},"What determines the choice of a machine learning algorithm in this cheat sheet?","Question",{"text":56,"@type":57},"The decision is driven by the nature of the data and the goal to achieve with that data for predictive analytics.","Answer",{"name":59,"@type":54,"acceptedAnswer":60},"Which methods are listed for text analytics tasks?",{"text":61,"@type":57},"It includes operations like preprocessing, feature extraction such as N-gram feature extraction and feature hashing, and approaches like Word2Vec for converting text into integer or value-encoded features.",{"name":63,"@type":54,"acceptedAnswer":64},"What kinds of problems does the cheat sheet cover beyond classification?",{"text":65,"@type":57},"It also covers regression (forecasting relationships and counts), recommenders (e.g., SVD and collaborative filtering), clustering (grouping similar data like K-Means), and anomaly detection including image classification.","https://schema.org",{"og:url":32,"og:type":68,"og:title":10,"og:site_name":40,"og:description":12},"article",{"robots":70,"canonical":32},"index,follow",{"doc_id":72,"site_id":7},573206,1791934612,{"code":4,"msg":75,"data":76},"success",[77,82,87,92,96,101,106,111,116],{"id":78,"doc_module":22,"doc_module_name":25,"category_name":79,"show_sort_weight":80,"slug":81},11,"Presentations",90,"presentations",{"id":83,"doc_module":22,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},12,"Resumes",80,"resumes",{"id":88,"doc_module":22,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},14,"Invoices",70,"invoices",{"id":93,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":94,"slug":95},15,60,"posters",{"id":97,"doc_module":22,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},16,"Social Media",50,"social-media",{"id":102,"doc_module":22,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},17,"Forms",40,"forms",{"id":107,"doc_module":22,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},18,"Letters",30,"letters",{"id":112,"doc_module":22,"doc_module_name":25,"category_name":113,"show_sort_weight":114,"slug":115},21,"Paper Templates",5,"papers-templates",{"id":117,"doc_module":22,"doc_module_name":25,"category_name":118,"show_sort_weight":4,"slug":119},158,"General","general-158",{"code":4,"msg":75,"data":121},{"doc_id":72,"user_id":122,"nickname":37,"user_avatar":123,"doc_module":22,"category_id":93,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":124,"file_id":125,"file_url":126,"file_type":127,"file_size":128,"view_count":4,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":129,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":130,"faqs":131,"seo_title":132,"seo_description":12,"update_tm":73,"read_time":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","Machine Learning Algorithm Cheat Sheet  \nThis cheat sheet helps you choose the best machine learning algorithm foryour predictive analytics solution. Your decision is driven by both the nature of your data and the goal you want to achieve with your data.  \nText Analytics  \nDerives high-quality information from text  \nAnswers questions like: What info is in this text?  \nExtract N-Gram Features from Text  \nFeature Hashing  \nPreprocess Text  \nWord2Vector  \nCreates a dictionary of n-grams from a column of free text  \nConverts text data to integer encoded features using the Vowpal Wabbit library  \nPerforms cleaning operations on text, like removal of stop-words, case normalization  \nConverts words to values for use in NLP tasks, like recommender, named entity recognition, machine translation  \nExtract information from text  \nGenerate recommendations  \nPredict between several categories  \nPredict between two categories  \nMulticlass Classification  \nAnswers complex questions with multiple possible answers  \nAnswers questions like: Is this A or B or C or D?  \nMulticlass Logistic Regression  \nMulticlass Neural Network  \nMulticlass Decision Forest  \nOne-vs-All Multiclass  \nMulticlass Boosted Decision Tree  \nFast training times, linear model  \nAccuracy, long training times  \nAccuracy, fast training times  \nDepends on the two-class classifier  \nNon-parametric, fast  \ntraining times and scalable  \nRecommenders  \nRegression  \nMakes forecasts by estimating the relationship between values  \nAnswers questions like: How much or how many?  \nFast Forest Quantile Regression  \nPoisson Regression  \nLinear Regression  \nBayesian Linear Regression  \nDecision Forest Regression  \nNeural Network Regression  \nBoosted Decision Tree Regression  \nPredicts a distribution  \nPredicts event counts  \nFast training, linear model  \nLinear model, small data sets  \nAccurate, fast training times  \nAccurate, long training times  \nAccurate, fast training times, large memory footprint  \nPredict values  \nPredicts what someone will be interested in  \nAnswers the question: What will they be interested in?  \nSVD Recommender  \nCollaborative filtering, better performance with lower cost by reducing dimensionality  \nDiscover structure  \nClustering  \nSeparates similar data points into intuitive groups  \nAnswers questions like: How is this organized?  \nK-Means Unsupervised learning  \nFind unusual occurrences Classify  \nimages  \nTwo-Class Classification  \nAnswers simple two-choice questions, like yes or no, true or false  \nAnswers questions like: Is this A or B?  \nTwo-Class Support Vector Machine  \nTwo-Class Averaged Perceptron  \nTwo-Class Decision Forest  \nTwo-Class Logistic Regression  \nTwo-Class Boosted Decision Tree  \nTwo-Class Neural Network  \nUnder 100 features, linear model  \nFast training, linear model  \nAccurate, fast training  \nFast training, linear model  \nAccurate, fast training, large memory footprint  \nAccurate, long training times  \nAnomaly Detection Image Classification  \nIdentifies and predicts rare or unusual data points Classifies images with popular networks  \nAnswers the question: Is this weird? Answers questions like: What does this image represent?  \nOne Class SVM  \nUnder 100 features, aggressive boundary  \nPCA-Based Anomaly Detection  \nFast training times DenseNet  \nHigh accuracy, better efficiency  \n© 2019 Microsoft Corporation. All rights reserved. Share this poster: [aka.ms/mlcheatsheet](aka.ms/mlcheatsheet)","cbCaidZTGEDw99ri","https://ap.wps.com/l/cbCaidZTGEDw99ri","pdf",214677,"English","# Text Analytics\n## Feature extraction and preprocessing\n# Classification\n## Multiclass classification\n## Two-class classification\n## Anomaly detection and image classification\n# Regression and Recommendor Systems\n## Regression methods\n## Recommenders and collaborative filtering\n# Clustering and Unsupervised Learning\n## K-Means clustering","[{\"question\":\"What determines the choice of a machine learning algorithm in this cheat sheet?\",\"answer\":\"The decision is driven by the nature of the data and the goal to achieve with that data for predictive analytics.\"},{\"question\":\"Which methods are listed for text analytics tasks?\",\"answer\":\"It includes operations like preprocessing, feature extraction such as N-gram feature extraction and feature hashing, and approaches like Word2Vec for converting text into integer or value-encoded features.\"},{\"question\":\"What kinds of problems does the cheat sheet cover beyond classification?\",\"answer\":\"It also covers regression (forecasting relationships and counts), recommenders (e.g., SVD and collaborative filtering), clustering (grouping similar data like K-Means), and anomaly detection including image classification.\"}]","Machine Learning Algorithm Cheat Sheet - general guide for choosing algorithms | PDF"]