High resolution PDFs of the figures can be found on a github repository.

Links by chapter:

• Chapter 1 Introduction
• Chapter 2 A Short Tour of the Predictive Modeling Process
• Chapter 3 Data Pre-Processing
• Chapter 4 Over-Fitting and Model Tuning
• Chapter 5 Measuring Performance in Regression Models
• Chapter 6 Linear Regression and Its Cousins
• Chapter 7 Non-Linear Regression Models
• Chapter 8 Regression Trees and Rule-Based Models
• Chapter 9 A Summary of Solubility Models
• Chapter 10 Case Study: Compressive Strength of Concrete Mixtures
• Chapter 11 Measuring Performance in Classification Models
• Chapter 12 Discriminant Analysis and Other Linear Classification Models
• Chapter 13 Non-Linear Classification Models
• Chapter 14 Classification Trees and Rule-Based Models
• Chapter 15 A Summary of Grant Application Models
• Chapter 16 Remedies for Severe Class Imbalance
• Chapter 17 Case Study: Job Scheduling
• Chapter 18 Measuring Predictor Importance
• Chapter 19 An Introduction to Feature Selection
• Chapter 20 Factors That Can Affect Model Performance
• Appendix B An Introduction to R