This part of the book focuses on black-box models and understanding how the model processes the inputs and arrives at the final prediction.
In chapter 3, you’ll learn about a class of black-box models called tree ensembles. You will learn about their characteristics and what makes them black-box. You’ll also learn how to interpret them using post hoc model-agnostic methods that are global in scope, such as partial dependence plots (PDPs) and feature interaction plots.
In chapter 4, you’ll learn about deep neural networks, specifically the vanilla fully connected neural networks. You will learn about characteristics that make these models black-box and how to interpret them using post hoc model-agnostic methods that are local in scope, such as local interpretable model-agnostic explanations (LIME), SHapley Additive exPlanations (SHAP), and anchors.
In chapter 5, you’ll learn about convolutional neural networks, which are a more advanced form of architecture used mainly for visual tasks, such as image classification and object detection. You’ll learn how to visualize what the model is focusing on using saliency maps. You will also learn about techniques such as gradients, guided backpropagation (backprop for short), gradient-weighted class activation mapping (Grad-CAM), guided Grad-CAM, and smooth gradients (SmoothGrad).