Description
Book SynopsisTable of ContentsForeword xix
Preface to the Fourth Edition xxi
Acknowledgments xxv
PART I PRELIMINARIES
CHAPTER 1 Introduction 3
CHAPTER 2 Overview of the Machine Learning Process 15
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization 59
CHAPTER 4 Dimension Reduction 91
PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance 115
PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression 151
CHAPTER 7 k-Nearest-Neighbors (k-NN) 169
CHAPTER 8 The Naive Bayes Classifier 181
CHAPTER 9 Classification and Regression Trees 197
CHAPTER 10 Logistic Regression 229
CHAPTER 11 Neural Nets 257
CHAPTER 12 Discriminant Analysis 283
CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303
PART V INTERVENTION AND USER FEEDBACK
CHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319
PART VI MINING RELATIONSHIPS AMONG RECORDS
CHAPTER 15 Association Rules and Collaborative Filtering 341
CHAPTER 16 Cluster Analysis 369
PART VII FORECASTING TIME SERIES
CHAPTER 17 Handling Time Series 401
CHAPTER 18 Regression-Based Forecasting 415
CHAPTER 19 Smoothing Methods 445
PART VIII DATA ANALYTICS
CHAPTER 20 Social Network Analytics 467
CHAPTER 21 Text Mining 487
CHAPTER 22 Responsible Data Science 507
PART IX CASES
CHAPTER 23 Cases 537
References 575
Data Files Used in the Book 577
Index 579
