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

3.5      Summary