Interpreting Machine Learning Models in Python with SHAP

In machine learning, understanding how models arrive at their predictions is crucial. A common way to determine feature contribution is by looking at feature importance. This measure is based on the decrease in model performance when removing a feature. It is a useful measure but contains no information beyond that importance.