3 Model-agnostic methods: Global interpretability
- 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
3.2.1 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
