Explaining Tree Based Models Using SHAP

In this 2-hour long project-based course, you will learn how to interpret or explain the output of tree based ensemble machine learning models. You will generate shapely values for all the features for each observations in the dataset. You will then learn to generate global and local explainability plots and then interpret it. You will learn how to create different shap plots for interpretability like - waterfall plot, force plot, decision plot etc. and also understand the use cases for each of these plots.


Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.