Using SHAP to Explain Machine Learning Models

Do you understand how your machine learning model works? Despite the ever-increasing usage of machine learning (ML) and deep learning (DL) techniques, the majority of companies say they can't explain the decisions of their ML algorithms [1]. This is, at least in part, due to the increasing complexity of both the data and models used. It's not easy to find a nice, stable aggregation over 100 decision trees in a random forest to say which features were most important or how the model came to the conclusion it did. This problem grows even more complex in application domains such as computer vision (CV) or natural language processing (NLP), where we no longer have the same high-level, understandable features to help us understand the model's failures.