Selecting a right Machine Learning algorithm for predictive analytics needs: Classification vs Regression vs Clustering

An interesting cheat sheet (a nice infographic!) was published by Microsoft sometime back to help beginning data scientists on how to choose a Machine Learning algorithm for different predictive analytics needs: Classification (to predict categories), Clustering (to discover structure), Regression (to predict values) and Anomaly Detection (to find unusual data points).

Here’s what Brandon, the author of the article “How to choose algorithms for Microsoft Azure Machine Learning”, says about it: “It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can’t tell which algorithm will perform best before trying them.”

Machine Learning Algorithm Selection - Predictive Analytics
Machine Learning Algorithm Selection – Predictive Analytics

The cheat sheet can be downloaded from http://aka.ms/MLCheatSheet

Another interesting read:

http://www.bigdataanalyticsguide.com/2016/06/20/master-machine-learning-widely-used-analytics-techniques-and-algorithms/

 

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