The contributed chapter covers an analysis of a random regression forest (implemented in the ranger package) on data extracted from the FIFA video game.
In more detail, the following methods for explainable machine learning are showcased:
- Dataset level exploration: Feature importance and Partial dependency plots.
- Instance level explanation: Break Down, SHapley Additive exPlanations (SHAP), and Ceteris Paribus plots.
Here is a small preview illustrating the effect of different features on the monetary value of Cristiano Ronaldo:
Read the complete chapter here.