I’m going to go through a typical workflow for developing a new visualizer for the Yellowbrick library including writing tests including visual unit tests and mocking. A visualizer extend the Scikit-Learn API to allow you to visually steer the modeling process. Along the way, I’m going to talk about some great utilities that are included in scikit-learn, matplotlib and numpy that might make your life easier.

Yellowbrick is a machine learning visualization library with the end goal of allowing data scientists to visually steer the model selection process through the production of high quality and information visualizations. Under the hood, Yellowbrick is built primarily on scikit-learn and matplotlib with a bit of numpy to glue it all together.