One of the big issues I’ve encountered in my teaching is explaining how to evaluate the performance of machine learning models. Simply put, it is relatively trivial to generate the various performance metrics–accuracy, precision, recall, etc–if you wanted to visualize any of these metrics, there wasn’t really an easy way to do that. Until now….
Recently, I learned of a new python library called YellowBrick, developed by Ben Bengfort at District Data Labs, that implements many different visualizations that are useful for building machine learning models and assessing their performance. Many visualization libraries require you to write a lot of “boilerplate” code: IE just generic and repetitive code, however what impressed me about YellowBrick is that it largely follows the scikit-learn API, and therefore if you are a regular user of scikit-learn, you’ll have no problem incorporating YellowBrick into your workflow. YellowBrick appears to be relatively new, so there still are definitely some kinks to be worked out, but overall, this is a really impressive library.