Applied-Predictive-Modeling

Max Kuhn, Kjell Johnson

This is a book on data analysis with a specific focus on the practice of predictive modeling. The term predictive modeling may stir associations such as machine learning, pattern recognition, and data mining. Indeed, these associations are appropriate and the methods implied by these terms are an integral piece of the predictive modeling process. But predictive modeling encompasses much more than the tools and techniques for uncovering patterns within data. The practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model’s prediction accuracy on future, yet-to-be-seen data. The entire process is the focus of this book.

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

20181201 First view

20190525 Second review