How to Find Weaknesses in your Machine Learning Models

Any time you simplify data using a summary statistic, you lose information. Model accuracy is no different. When simplifying your model's fit to a summary statistic, you lose the ability to determine where your performance is lowest/highest and why. In this post we discuss the code behind IBM's FreaAI, an efficient method for identifying data slices with low accuracy. In prior posts, we covered the method at a high level and did a deep dive into leveraging HPD to find areas of model weakness. Here, we will walk through a an MVP implementation of the paper for a binary classifier.