Preface to the RapidMiner Edition xxiii

Acknowledgments xxvii

Part I Preliminaries

Chapter 1 Introduction 3

1.1 What Is Business Analytics? 3

1.2 What Is Machine Learning? 5

1.3 Machine Learning, AI, and Related Terms 5

1.4 Big Data 7

1.5 Data Science 8

1.6 Why Are There So Many Different Methods? 9

1.7 Terminology and Notation 9

1.8 Road Maps to This Book 12

1.9 Using RapidMiner Studio 14

Chapter 2 Overview of the Machine Learning Process 19

2.1 Introduction 19

2.2 Core Ideas in Machine Learning 20

2.3 The Steps in a Machine Learning Project 23

2.4 Preliminary Steps 25

2.5 Predictive Power and Overfitting 32

2.6 Building a Predictive Model with RapidMiner 37

2.7 Using RapidMiner for Machine Learning 45

2.8 Automating Machine Learning Solutions 47

2.9 Ethical Practice in Machine Learning 52

Problems 57

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization 63

3.1 Introduction 63

3.2 Data Examples 65

3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66

3.4 Multidimensional Visualization 75

3.5 Specialized Visualizations 87

3.6 Summary: Major Visualizations and Operations, by Machine Learning Goal 92

Chapter 4 Dimension Reduction 97

4.1 Introduction 97

4.2 Curse of Dimensionality 98

4.3 Practical Considerations 98

4.4 Data Summaries 100

4.5 Correlation Analysis 103

4.6 Reducing the Number of Categories in Categorical Attributes 105

4.7 Converting a Categorical Attribute to a Numerical Attribute 107

4.8 Principal Component Analysis 107

4.9 Dimension Reduction Using Regression Models 117

4.10 Dimension Reduction Using Classification and Regression Trees 119

Problems 120

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance 125

5.1 Introduction 125

5.2 Evaluating Predictive Performance 126

5.3 Judging Classifier Performance 131

5.4 Judging Ranking Performance 146

5.5 Oversampling 151

Problems 158

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression 163

6.1 Introduction 163

6.2 Explanatory vs. Predictive Modeling 164

6.3 Estimating the Regression Equation and Prediction 166

6.4 Variable Selection in Linear Regression 171

Problems 184

Chapter 7 k-Nearest Neighbors (k-NN) 189

7.1 The k-NN Classifier (Categorical Label) 189

7.2 k-NN for a Numerical Label 200

7.3 Advantages and Shortcomings of k-NN Algorithms 202

Appendix: Computing Distances Between Records in RapidMiner 203

Problems 205

Chapter 8 The Naive Bayes Classifier 209

8.1 Introduction 209

8.2 Applying the Full (Exact) Bayesian Classifier 211

8.3 Solution: Naive Bayes 213

8.4 Advantages and Shortcomings of the Naive Bayes Classifier 224

Problems 226

Chapter 9 Classification and Regression Trees 229

9.1 Introduction 229

9.2 Classification Trees 232

9.3 Evaluating the Performance of a Classification Tree 240

9.4 Avoiding Overfitting 245

9.5 Classification Rules from Trees 255

9.6 Classification Trees for More Than Two Classes 256

9.7 Regression Trees 256

9.8 Improving Prediction: Random Forests and Boosted Trees 259

9.9 Advantages and Weaknesses of a Tree 261

Problems 265

Chapter 10 Logistic Regression 269

10.1 Introduction 269

10.2 The Logistic Regression Model 271

10.3 Example: Acceptance of Personal Loan 272

10.4 Logistic Regression for Multi-class Classification 283

10.5 Example of Complete Analysis: Predicting Delayed Flights 286

Appendix: Logistic Regression for Ordinal Classes 299

Problems 301

Chapter 11 Neural Networks 305

11.1 Introduction 306

11.2 Concept and Structure of a Neural Network 306

11.3 Fitting a Network to Data 307

11.4 Required User Input 321

11.5 Exploring the Relationship Between Predictors and Target Attribute 322

11.6 Deep Learning 323

11.7 Advantages and Weaknesses of Neural Networks 334

Problems 335

Chapter 12 Discriminant Analysis 337

12.1 Introduction 337

12.2 Distance of a Record from a Class 340

12.3 Fisher’s Linear Classification Functions 341

12.4 Classification Performance of Discriminant Analysis 346

12.5 Prior Probabilities 348

12.6 Unequal Misclassification Costs 348

12.7 Classifying More Than Two Classes 349

12.8 Advantages and Weaknesses 351

Problems 355

Chapter 13 Generating, Comparing, and Combining Multiple Models 359

13.1 Automated Machine Learning (AutoML) 359

13.2 Explaining Model Predictions 367

13.3 Ensembles 373

13.4 Summary 381

Problems 383

Part V Intervention and User Feedback

Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 387

14.1 A/B Testing 387

14.2 Uplift (Persuasion) Modeling 393

14.3 Reinforcement Learning 400

14.4 Summary 405

Problems 406

Part VI Mining Relationships Among Records

Chapter 15 Association Rules and Collaborative Filtering 409

15.1 Association Rules 409

15.2 Collaborative Filtering 424

15.3 Summary 438

Problems 440

Chapter 16 Cluster Analysis 445

16.1 Introduction 445

16.2 Measuring Distance Between Two Records 449

16.3 Measuring Distance Between Two Clusters 455

16.4 Hierarchical (Agglomerative) Clustering 457

16.5 Non-Hierarchical Clustering: The k-Means Algorithm 466

Problems 473

Part VII Forecasting Time Series

Chapter 17 Handling Time Series 479

17.1 Introduction 480

17.2 Descriptive vs. Predictive Modeling 481

17.3 Popular Forecasting Methods in Business 481

17.4 Time Series Components 482

17.5 Data Partitioning and Performance Evaluation 486

Problems 493

Chapter 18 Regression-Based Forecasting 497

18.1 A Model with Trend 498

18.2 A Model with Seasonality 505

18.3 A Model with Trend and Seasonality 508

18.4 Autocorrelation and ARIMA Models 509

Problems 521

Chapter 19 Smoothing and Deep Learning Methods for Forecasting 533

19.1 Smoothing Methods: Introduction 534

19.2 Moving Average 534

19.3 Simple Exponential Smoothing 540

19.4 Advanced Exponential Smoothing 545

19.5 Deep Learning for Forecasting 549

Problems 553

Part VIII Data Analytics

Chapter 20 Social Network Analytics 563

20.1 Introduction 563

20.2 Directed vs. Undirected Networks 564

20.3 Visualizing and Analyzing Networks 567

20.4 Social Data Metrics and Taxonomy 571

20.5 Using Network Metrics in Prediction and Classification 576

20.6 Collecting Social Network Data with RapidMiner 584

20.7 Advantages and Disadvantages 584

Problems 587

Chapter 21 Text Mining 589

21.1 Introduction 589

21.2 The Tabular Representation of Text: Term–Document Matrix and “Bag-of-Words’’ 590

21.3 Bag-of-Words vs. Meaning Extraction at Document Level 592

21.4 Preprocessing the Text 593

21.5 Implementing Machine Learning Methods 602

21.6 Example: Online Discussions on Autos and Electronics 602

21.7 Example: Sentiment Analysis of Movie Reviews 607

21.8 Summary 614

Problems 615

Chapter 22 Responsible Data Science 617

22.1 Introduction 617

22.2 Unintentional Harm 618

22.3 Legal Considerations 620

22.4 Principles of Responsible Data Science 621

22.5 A Responsible Data Science Framework 624

22.6 Documentation Tools 628

22.7 Example: Applying the RDS Framework to the COMPAS Example 631

22.8 Summary 641

Problems 643

Part IX Cases

Chapter 23 Cases 647

23.1 Charles Book Club 647

23.2 German Credit 654

23.3 Tayko Software Cataloger 659

23.4 Political Persuasion 663

23.5 Taxi Cancellations 667

23.6 Segmenting Consumers of Bath Soap 669

23.7 Direct-Mail Fundraising 673

23.8 Catalog Cross-Selling 676

23.9 Time Series Case: Forecasting Public Transportation Demand 678

23.10 Loan Approval 680

References 683

Data Files Used in the Book 687

Index 689