Interpretable AI

4 Model-agnostic methods: Local interpretability

This chapter covers

• Characteristics of deep neural networks
• How to implement deep neural networks that are inherently black-box models
• Perturbation-based model-agnostic methods that are local in scope, such as LIME, SHAP and anchors
• How to interpret deep neural networks using LIME, SHAP, and anchors
• Strengths and weaknesses of LIME, SHAP, and anchors

In the previous chapter, we looked at tree ensembles, especially random forest models, and learned how to interpret them using model-agnostic methods that are global in scope, such as partial dependence plots (PDPs) and feature interaction plots. We saw that PDPs are a great way of understanding how individual feature values impact the final model ...