Microsoft Azure Machine Learning - Algorithm Cheat Sheet - v6 - choose the best Studio algorithms

Oct 13, 2026

Upload by Finn

Microsoft Azure Machine Learning Algorithm Cheat Sheet condenses guidance for selecting Azure Machine Learning Studio algorithms for predictive analytics. The sheet organizes model types by task—anomaly detection, clustering, multi-class classification, two-class classification, and regression—and pairs each with practical decision factors such as expected data characteristics, feature count thresholds, training speed, linear vs nonlinear behavior, memory footprint, and output type (categories, ranks, or event counts). It supports faster, more informed algorithm selection in model planning.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages1

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ANOMALY DETECTION

CLUSTERING

MULTI-CLASS CLASSIFICATION

TWO-CLASS CLASSIFICATION

REGRESSION

Microsoft Azure Machine Learning: Algorithm Cheat Sheet This cheat sheet helps you choose the best Azure Machine Learning Studio algorithm for your predictive analytics solution. Your decision is driven by both the nature of your data and the question you’re trying to answer. ANOMALY DETECTION One-class SVM >100 features, aggressive boundary PCA-based anomaly detection Fast training CLUSTERING K-means START Discovering structure Finding unusual data points values Predicting Predicting categories Three or more Two MULTI-CLASS CLASSIFICATION ~~ ~~ Fast training, linear model ~~ ~~ Accuracy, long training times ~~ ~~ Accuracy, fast training ~~ ~~ Accuracy, small memory footprint Depends on the two-class classifier, see notes below Multiclass logistic regression Multiclass neural network Multiclass decision forest Multiclass decision jungle One-v-all multiclass TWO-CLASS CLASSIFICATION Two-class SVM Two-class averaged perceptron Two-class logistic regression Two-class Bayes point machine Accuracy, fast training >100 features, linear model Fast training, linear model Fast training, linear model Fast training, linear model Accuracy, fast training, large memory footprint Accuracy, small memory footprint >100 features Accuracy, long training times Two-class decision forest Two-class boosted decision tree Two-class decision jungle Two-class locally deep SVM Two-class neural network REGRESSION Ordinal regression Data in rank ordered categories Poisson regression Predicting event counts Fast forest quantile regression Predicting a distribution Linear regression Fast training, linear model Linear model, small data sets Accuracy, long training time Accuracy, fast training Accuracy, fast training, large memory footprint Bayesian linear regression Neural network regression Decision forest regression Boosted decision tree regression © 2015 Microsoft Corporation. All rights reserved. Created by the Azure Machine Learning Team [Email: AzurePoster@microsoft.com](Email: AzurePoster@microsoft.com) Download this poster: [http://aka.ms/MLCheatSheet](http://aka.ms/MLCheatSheet)

FAQs

How does the cheat sheet help choose Azure Machine Learning Studio algorithms?

It links algorithm families to the predictive analytics task and selection drivers such as data nature, feature count, training time, and memory footprint.

What factors are emphasized for anomaly detection and clustering choices?

For anomaly detection it highlights approaches like one-class SVM and PCA-based methods with notes on boundary behavior and training speed. For clustering it lists K-means with guidance on discovering structure and identifying unusual points.

How are classification and regression algorithms organized in the sheet?

Classification is split into multi-class and two-class sections with multiple candidate models and performance notes. Regression is grouped by target form (rank-ordered categories, event counts, distributions, or linear vs nonlinear outputs) and corresponding model options.

Oct 13, 2026

Upload by Finn