应用预测建模：数据科学与机器学习方法

本书专注于预测建模的 实际应用，介绍了从数据预 处理到建模再到模型评估和 选择的整个过程，以及背后 的统计思想，涉及各种回归 技术和分类技术。从解决实 际问题延伸到模型拟合，以 及随之出现的主题，如处理 类不平衡、选择预测因子等 在实践中经常出现的问题， 作者意在为读者提供预测建 模过程的指导，并结合开源 软件R语言来求解实际问题 ，详细给出R代码和处理的 步骤。

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 Neighbors 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