Interpretable AI MEAP V02
Copyright
Welcome letter
Brief contents
Chapter 1: Introduction
1.1 Diagnostics+ AI β An Example AI System
1.2 Types of Machine Learning Systems
1.2.1 Representation of Data
1.2.2 Supervised Learning
1.2.3 Unsupervised Learning
1.2.4 Reinforcement Learning
1.2.5 Machine Learning System for Diagnostics+ AI
1.3 Building Diagnostics+ AI
1.4 Gaps in Diagnostics+ AI
1.4.1 Data Leakage
1.4.2 Bias
1.4.3 Regulatory Non-Compliance
1.4.4 Concept Drift
1.5 Building a Robust Diagnostics+ AI
1.6 Interpretability v/s Explainability
1.6.1 Types of Interpretability Techniques
1.7 What will I learn in this book?
1.7.1 What tools will I be using in this book?
1.7.2 What do I need to know before reading this book?
1.8 Summary
Chapter 2: White-Box Models
2.1 White-Box Models
2.1.1 Diagnostics+ AI β Diabetes Progression
2.2 Linear Regression
2.2.1 Interpreting Linear Regression
2.2.2 Limitations of Linear Regression
2.3 Decision Trees
2.3.1 Interpreting Decision Trees
2.3.2 Limitations of Decision Trees
2.4 Generalized Additive Models (GAMs)
2.4.1 Regression Splines
2.4.2 GAM for Diagnostics+ Diabetes
2.4.3 Interpreting GAMs
2.4.4 Limitations of GAMs
2.5 Looking Ahead to Black-Box Models
2.6 Summary
Chapter 3: Model Agnostic Methods - Global Interpretability
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 β Global Interpretability
3.4.1 Partial Dependence Plots
3.4.2 Feature Interactions
3.5 Summary
Chapter 4: Model Agnostic Methods β Local Interpretability
4.1 Diagnostics+ AI β Breast Cancer Diagnosis
4.2 Exploratory Data Analysis
4.3 Deep Neural Networks
4.3.1 Data Preparation
4.3.2 Training and Evaluating DNNs
4.4 Interpreting DNNs
4.5 LIME
4.6 SHAP
4.7 Anchors
4.8 Summary