Choosing Machine Learning Algorithms: Lessons from Microsoft Azure

Last Updated on August 12, 2019

Microsoft recently launched support for machine learning in their Azure cloud computing platform.

Buried in some of their technical documentation for the platform are some resources that you may find useful for thinking about what machine learning algorithm to use in different situations.

In this post we take a look at the Microsoft recommendations for machine learning algorithms and the lessons that we can use when working through machine learning problems on any platform.

Machine Learning Algorithm Cheatsheet

Microsoft released a PDF cheatsheet of what machine learning algorithms to use, when.

The one-pager lists various problem types as groups and the algorithms supported by Azure in each group.

These groups are:

• Regression: for predicting values.
• Anomaly detection: for finding unusual data points.
• Clustering: for discovering structure.
• Two-class classification: for predicting two categories.
• Multi-class classification: for predicting three or more categories.

The first problem with this approach is that the algorithm names seemingly map onto the Azure API
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