- 应用预测建模(英文版)
- - 作者：(美)M.库恩//K.约翰逊|责编:刘慧//高蓉
  - 出版社：世界图书出版公司
  - ISBN：9787519220891
  - 出版日期：2017/06/01
  - 页数：600
- 售价：79.6

内容大纲
本书是一部关于数据分析的经典教材，聚焦预测建模的实际应用，如如何进行数据预处理、模型调优、预测变量重要性度量、变量选择等。读者可以从中学到许多建模方法以及提高对许多常用的、现代的有效模型的认识，如线性回归、非线性回归和分类模型，涉及树方法、支持向量机等。书中还涉及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。
作者介绍
目录
  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 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 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
  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
    19.11 Consequences of Using Non-informative Predictors
    19.12 Approaches for Reducing the Number of Predictor
    19.13 Wrapper Methods
    19.14 Filter Methods
    19.15 Selection Bias
    19.16 Case Study: Predicting Cognitive Impairment
    19.17 Computing
    Exercises
  20 Factors That Can Affect Model Performance
    20.1 Type Ⅲ Errors
    20.2 Measurement Error in the Outcome
    20.3 Measurement Error in the Predictors
    20.4 Discretizing Continuous Outcomes
    20.5 When Should You Trust Your Model's Prediction?
    20.6 The Impact of a Large Sample
    20.7 Computing
    Exercises
Appendix
  A A Summary of Various Models
  B An Introduction to R
    B.1 Start-Up and Getting Help
    B.2 Packages
    B.3 Creating Objects
    B.4 Data Types and Basic Structures

    B.5 Working with Rectangular Data Sets
    B.6 Objects and Classes
    B.7 R Functions
    B.8 The Three Faces of =
    B.9 The AppliedPredictiveModeling Package
    B.10 The caret Package
    B.11 Software Used in this Text
  C Interesting Web Sites
References
Indicies
Computing
General

内容大纲

作者介绍

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