Applied predictive modeling

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process.

This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Introduction -- General Strategies. A Short Tour of the Predictive Modeling Process -- Data Pre-processing -- Over-Fitting and Model Tuning -- Regression Models. Measuring Performance in Regression Models -- Linear Regression and Its Cousins -- Nonlinear Regression Models -- Regression Trees and Rule-Based Models -- A Summary of Solubility Models -- Case Study: Compressive Strength of Concrete Mixtures -- Classification Models. Measuring Performance in Classification Models -- Discriminant Analysis and Other Linear Classification Models -- Nonlinear Classification Models -- Classification Trees and Rule-Based Models -- A Summary of Grant Application Models -- Remedies for Severe Class Imbalance -- Case Study: Job Scheduling -- Other Considerations. Measuring Predictor Importance -- An Introduction to Feature Selection -- Factors That Can Affect Model Performance.