Interpreting Machine Learning Models: An Overview

An article on machine learning interpretation appeared on O'Reilly's blog back in March, written by Patrick Hall, Wen Phan, and SriSatish Ambati, which outlined a number of methods beyond the usual go-to measures. By chance I happened back upon the article again over the weekend, and with a fresh read decided to share some of the ideas contained within. The article is a great (if lengthy) read, and recommend it to anyone who has the time. Part 1 includes approaches for seeing and understanding your data in the context of training and interpreting machine learning algorithms, Part 2 introduces techniques for combining linear models and machine learning algorithms for situations where interpretability is of paramount importance, and Part 3 describes approaches for understanding and validating the most complex types of predictive models. The deconstruction of the interpretability of each technique and group of techniques is the focus of the article, while this post is a summary of the techniques.