Applied predictive modeling

1 Introduction 1.1 Prediction Versus Interpretation 1.2 Key Ingredients of Predictive Models 1.3 Terminology 1.4 Example Data Sets and Typical Data Scenarios 1.5 Overview 1.6 Notation Part I General Strategies 2 A Short Tour of the Predictive Modeling Process 2.1 Case Study: Predicting Fuel Economy 2.2 Themes 2.3 Summary 3 Data Pre-processing 3.1 Case Study: Cell Segmentation in High-Content Screening 3.2 Data Transformations for Individual Predictors 3.3 Data Transformations for Multiple Predictors 3.4 Dealing with Missing Values 3.5 Removing Predictors 3.6 Adding Predictors 3.7 Binning Predictors 3.8 Computing Exercises 4 Over-Fitting and Model Tuning 4.1 The Problem of Over-Fitting 4.2 Model Tuning 4.3 Data Splitting 4.4 Resampling Techniques 4.5 Case Study: Credit Scoring 4.6 Choosing Final Tuning Parameters 4.7 Data Splitting Recommendations 4.8 Choosing Between Models 4.9 Computing Exercises Part II Regression Models 5 Measuring Performance in Regression Models 5.1 Quantitative Measures of Performance 5.2 The Variance-Bias Trade-off 5.3 Computing 6 Linear Regression and Its Cousins 6.1 Case Study: Quantitative Structure-Activity Relationship Modeling 6.2 Linear Regression 6.3 Partial Least Squares 6.4 Penalized Models 6.5 Computing Exercises 7 Nonlinear Regression Models 7.1 Neural Networks 7.2 Multivariate Adaptive Regression Splines 7.3 Support Vector Machines 7.4 K-Nearest Nei ors 7.5 Computing Exercises 8 Regression Trees and Rule-Based Models 8.1 Basic Regression Trees 8.2 Regression Model Trees 8.3 Rule-Based Models 8.4 Bagged Trees 8.5 Random Forests 8.6 Boosting 8.7 Cubist 8.8 Computing Exercises ¡­¡­ Part III Classification Models Appendix References Indicies Computing General