chapter three

3 Model-agnostic methods: Global interpretability

 

This chapter covers

  • Characteristics of model-agnostic methods and global interpretability
  • How to implement tree ensembles, specifically random forest—a black-box model
  • How to interpret random forest models
  • How to interpret black-box models using a model-agnostic method called partial dependence plots (PDPs)
  • How to uncover bias by looking at feature interactions

3.1 High school student performance predictor

3.1.1 Exploratory data analysis

3.2 Tree ensembles

3.2.1 Training a random forest

3.3 Interpreting a random forest

3.4 Model-agnostic methods: Global interpretability

3.4.1 Partial dependence plots

3.4.2 Feature interactions

Summary