●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 ltiple 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 Relationshir 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 ltivariate 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
9 A Summary of Solubility Models
10 Case Study:Compressive Strength of Concrete Mixtures
10.1 Model Building Strategy
10.2 Model Performance
10.3 Optimizing Compressive Strength
10.4 Computing
Part Ⅲ Classification Models
11 Measuring Performance in Classification Models
11.1 Class Predictions
11.2 Evaluating Predicted Classes
11.3 Evaluating Class Probabilities
11.4 Computing
12 Discriminant Analysis and Other Linear Classification Models
12.1 Case Study:Predicting Successful Grant Applications
12.2 Logistic Regression
12.3 Linear Discriminant Analysis
12.4 Partial Least Squares Discriminant Analysis
12.5 Penalized Models
12.6 Nearest Shrunken Centroids
12.7 Computing
Exercises
13 Nonlinear Classification Models
13.1 Nonlinear Discriminant Analysis
13.2 Neural Networks
13.3 Flexible Discriminant Analysis
13.4 Support Vector Machines
13.5 K-Nearest Neighbors
13.6 Naive Bayes
13.7 Computing
Exercises
14 Classification Trees and Rule-Based Models
14.1 Basic Classification Trees
14.2 Rule-Based Models
14.3 Bagged Trees
14.4 Random Forests
14.5 Boosting
14.6 C5.0
14.7 Comparing Two Encodings of Categorical Predictors
14.8 Computing
Exercises
15 A Summary of Grant Application Models
16 Remedies for Severe Class Imbalance
16.1 Case Study:Predicting Caravan Policy Ownership
16.2 The Effect of Class Imbalance
16.3 Model Tuning
16.4 Alternate Cutoffs
16.5 Adjusting Prior Probabilities
16.6 Unequal Case Weights
16.7 Sampling Methods
16.8 Cost-Sensitive Training
16.9 Computing
Exercises
17 Case Study:Job Scheduling
17.1 Data Splitting and Model Strategy
17.2 Results
17.3 Computing
Part Ⅳ Other Considerations
18 Measuring Predictor Importance
18.1 Numeric Outcomes
18.2 Categorical Outcomes
18.3 Other Approaches
18.4 Computing
Exercises
19 An Introduction to Feature Selection
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本書是一部關於數據分析的經典教材，聚焦預測建模的實際應用，如如何進行數據預處理、模型調優、預測變量重要性度量、變量選擇等。讀者可以從中學到許多建模方法以及提高對許多常用的、現代的有效模型的認識，如線性回歸、非線性回歸和分類模型，涉及樹方法、支持向量機等。書中還涉及從數據預處理到建模再到模型評估和選擇的整個過程，以及背後的統計思想，涉及各種回歸技術和分類技術。