Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R, Second Edition

Galit Shmueli, PhD, is Distinguished Professor and Institute Director at National Tsing Hua University's Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Peter C. Bruce, is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. Peter Gedeck, PhD, is Senior Data Scientist at Collaborative Drug Discovery and teaches at statistics.com and the UVA School of Data Science. His specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Inbal Yahav, PhD, is a Senior Lecturer in The Coller School of Management at Tel Aviv University, Israel. Her work focuses on the development and adaptation of statistical models for use by researchers in the field of information systems. Nitin R. Patel, PhD, is Co-founder and Lead Researcher at Cytel Inc. He was also a Co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University, USA.

Foreword by Ravi Bapna xix Foreword by Gareth James xxi Preface to the Second R Edition xxiii Acknowledgments xxvi 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? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 13 Chapter 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 Classification 18 Prediction 18 Association Rules and Recommendation Systems 18 Predictive Analytics 19 Data Reduction and Dimension Reduction 19 Data Exploration and Visualization 19 Supervised and Unsupervised Learning 20 2.3 The Steps in a Machine Learning Project 21 2.4 Preliminary Steps 23 Organization of Data 23 Predicting Home Values in the West Roxbury Neighborhood 23 Loading and Looking at the Data in R 24 Sampling from a Database 26 Oversampling Rare Events in Classification Tasks 27 Preprocessing and Cleaning the Data 28 2.5 Predictive Power and Overfitting 35 Overfitting 36 Creating and Using Data Partitions 38 2.6 Building a Predictive Model 41 Modeling Process 41 2.7 Using R for Machine Learning on a Local Machine 46 2.8 Automating Machine Learning Solutions 47 Predicting Power Generator Failure 48 Uber's Michelangelo 50 2.9 Ethical Practice in Machine Learning 52 Machine Learning Software: The State of the Market (by Herb Edelstein) 53 Problems 57 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 63 3.1 Uses of Data Visualization 63 Base R or ggplot? 65 3.2 Data Examples 65 Example 1: Boston Housing Data 65 Example 2: Ridership on Amtrak Trains 67 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 67 Distribution Plots: Boxplots and Histograms 70 Heatmaps: Visualizing Correlations and Missing Values 73 3.4 Multidimensional Visualization 75 Adding Variables: Color, Size, Shape, Multiple Panels, and Animation 76 Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 79 Reference: Trend Lines and Labels 83 Scaling Up to Large Datasets 85 Multivariate Plot: Parallel Coordinates Plot 85 Interactive Visualization 88 3.5 Specialized Visualizations 91 Visualizing Networked Data 91 Visualizing Hierarchical Data: Treemaps 93 Visualizing Geographical Data: Map Charts 95 3.6 Major Visualizations and Operations, by Machine Learning Goal 97 Prediction 97 Classification 97 Time Series Forecasting 97 Unsupervised Learning 98 Problems 99 Chapter 4 Dimension Reduction 101 4.1 Introduction 101 4.2 Curse of Dimensionality 102 4.3 Practical Considerations 102 Example 1: House Prices in Boston 103 4.4 Data Summaries 103 Summary Statistics 104 Aggregation and Pivot Tables 104 4.5 Correlation Analysis 107 4.6 Reducing the Number of Categories in Categorical Variables 109 4.7 Converting a Categorical Variable to a Numerical Variable 111 4.8 Principal Component Analysis 111 Example 2: Breakfast Cereals 111 Principal Components 116 Normalizing the Data 117 Using Principal Components for Classification and Prediction 120 4.9 Dimension Reduction Using Regression Models 121 4.10 Dimension Reduction Using Classification and Regression Trees 121 Problems 123 Part III Performance Evaluation Chapter 5 Evaluating Predictive Performance 129 5.1 Introduction 130 5.2 Evaluating Predictive Performance 130 Naive Benchmark: The Average 131 Prediction Accuracy Measures 131 Comparing Training and Holdout Performance 133 Cumulative Gains and Lift Charts 133 5.3 Judging Classifier Performance 136 Benchmark: The Naive Rule 136 Class Separation 136 The Confusion (Classification) Matrix 137 Using the Holdout Data 138 Accuracy Measures 139 Propensities and Threshold for Classification 139 Performance in Case of Unequal Importance of Classes 143 Asymmetric Misclassification Costs 146 Generalization to More Than Two Classes 149 5.4 Judging Ranking Performance 150 Cumulative Gains and Lift Charts for Binary Data 150 Decile-wise Lift Charts 153 Beyond Two Classes 154 Gains and Lift Charts Incorporating Costs and Benefits 154 Cumulative Gains as a Function of Threshold 155 5.5 Oversampling 156 Creating an Over-sampled Training Set 158 Evaluating Model Performance Using a Non-oversampled Holdout Set 159 Evaluating Model Performance If Only Oversampled Holdout Set Exists 159 Problems 162 Part IV Prediction and Classification Methods Chapter 6 Multiple Linear Regression 167 6.1 Introduction 167 6.2 Explanatory vs. Predictive Modeling 168 6.3 Estimating the Regression Equation and Prediction 170 Example: Predicting the Price of Used Toyota Corolla Cars 171 Cross-validation and caret 175 6.4 Variable Selection in Linear Regression 176 Reducing the Number of Predictors 176 How to Reduce the Number of Predictors 178 Regularization (Shrinkage Models) 183 Problems 188 Chapter 7 k-Nearest Neighbors (kNN) 193 7.1 The k-NN Classifier (Categorical Outcome) 193 Determining Neighbors 194 Classification Rule 194 Example: Riding Mowers 195 Choosing k 196 Weighted k-NN 199 Setting the Cutoff Value 200 k-NN with More Than Two Classes 201 Converting Categorical Variables to Binary Dummies 201 7.2 k-NN for a Numerical Outcome 201 7.3 Advantages and Shortcomings of k-NN Algorithms 204 Problems 205 Chapter 8 The Naive Bayes Classifier 207 8.1 Introduction 207 Threshold Probability Method 208 Conditional Probability 208 Example 1: Predicting Fraudulent Financial Reporting 208 8.2 Applying the Full (Exact) Bayesian Classifier 209 Using the "Assign to the Most Probable Class" Method 210 Using the Threshold Probability Method 210 Practical Difficulty with the Complete (Exact) Bayes Procedure 210 8.3 Solution: Naive Bayes 211 The Naive Bayes Assumption of Conditional Independence 212 Using the Threshold Probability Method 212 Example 2: Predicting Fraudulent Financial Reports, Two Predictors 213 Example 3: Predicting Delayed Flights 214 Working with Continuous Predictors 218 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 220 Problems 223 Chapter 9 Classification and Regression Trees 225 9.1 Introduction 226 Tree Structure 227 Decision Rules 227 Classifying a New Record 227 9.2 Classification Trees 228 Recursive Partitioning 228 Example 1: Riding Mowers 228 Measures of Impurity 231 9.3 Evaluating the Performance of a Classification Tree 235 Example 2: Acceptance of Personal Loan 236 9.4 Avoiding Overfitting 239 Stopping Tree Growth 242 Pruning the Tree 243 Best-Pruned Tree 245 9.5 Classification Rules from Trees 247 9.6 Classification Trees for More Than Two Classes 248 9.7 Regression Trees 249 Prediction 250 Measuring Impurity 250 Evaluating Performance 250 9.8 Advantages and Weaknesses of a Tree 250 9.9 Improving Prediction: Random Forests and Boosted Trees 252 Random Forests 252 Boosted Trees 254 Problems 257 Chapter 10 Logistic Regression 261 10.1 Introduction 261 10.2 The Logistic Regression Model 263 10.3 Example: Acceptance of Personal Loan 264 Model with a Single Predictor 265 Estimating the Logistic Model from Data: Computing Parameter Estimates 267 Interpreting Results in Terms of Odds (for a Profiling Goal) 270 10.4 Evaluating Classification Performance 271 10.5 Variable Selection 273 10.6 Logistic Regression for Multi-Class Classification 274 Ordinal Classes 275 Nominal Classes 276 10.7 Example of Complete Analysis: Predicting Delayed Flights 277 Data Preprocessing 282 Model-Fitting and Estimation 282 Model Interpretation 282 Model Performance 284 Variable Selection 285 Problems 289 Chapter 11 Neural Nets 293 11.1 Introduction 293 11.2 Concept and Structure of a Neural Network 294 11.3 Fitting a Network to Data 295 Example 1: Tiny Dataset 295 Computing Output of Nodes 296 Preprocessing the Data 299 Training the Model 300 Example 2: Classifying Accident Severity 304 Avoiding Overfitting 305 Using the Output for Prediction and Classification 305 11.4 Required User Input 307 11.5 Exploring the Relationship Between Predictors and Outcome 308 11.6 Deep Learning 309 Convolutional Neural Networks (CNNs) 310 Local Feature Map 311 A Hierarchy of Features 311 The Learning Process 312 Unsupervised Learning 312 Example: Classification of Fashion Images 313 Conclusion 320 11.7 Advantages and Weaknesses of Neural Networks 320 Problems 322 Chapter 12 Discriminant Analysis 325 12.1 Introduction 325 Example 1: Riding Mowers 326 Example 2: Personal Loan Acceptance 327 12.2 Distance of a Record from a Class 327 12.3 Fisher's Linear Classification Functions 329 12.4 Classification Performance of Discriminant Analysis 333 12.5 Prior Probabilities 334 12.6 Unequal Misclassification Costs 334 12.7 Classifying More Than Two Classes 336 Example 3: Medical Dispatch to Accident Scenes 336 12.8 Advantages and Weaknesses 339 Problems 341 Chapter 13 Generating, Comparing, and Combining Multiple Models 345 13.1 Ensembles 346 Why Ensembles Can Improve Predictive Power 346 Simple Averaging or Voting 348 Bagging 349 Boosting 349 Bagging and Boosting in R 349 Stacking 350 Advantages and Weaknesses of Ensembles 351 13.2 Automated Machine Learning (AutoML) 352 AutoML: Explore and Clean Data 352 AutoML: Determine Machine Learning Task 353 AutoML: Choose Features and Machine Learning Methods 354 AutoML: Evaluate Model Performance 354 AutoML: Model Deployment 356 Advantages and Weaknesses of Automated Machine Learning 357 13.3 Explaining Model Predictions 358 13.4 Summary 360 Problems 362 345 Part V Intervention and User Feedback Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 367 14.1 A/B Testing 368 Example: Testing a New Feature in a Photo Sharing App 369 The Statistical Test for Comparing Two Groups (T-Test) 370 Multiple Treatment Groups: A/B/n Tests 372 Multiple A/B Tests and the Danger of Multiple Testing 372 14.2 Uplift (Persuasion) Modeling 373 Gathering the Data 374 A Simple Model 376 Modeling Individual Uplift 376 Computing Uplift with R 378 Using the Results of an Uplift Model 378 14.3 Reinforcement Learning 380 Explore-Exploit: Multi-armed Bandits 380 Example of Using a Contextual Multi-Arm Bandit for Movie Recommendations 382 Markov Decision Process (MDP) 383 14.4 Summary 388 Problems 390 Part VI Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 393 15.1 Association Rules 394 Discovering Association Rules in Transaction Databases 394 Example 1: Synthetic Data on Purchases of Phone Faceplates 394 Generating Candidate Rules 395 The Apriori Algorithm 397 Selecting Strong Rules 397 Data Format 399 The Process of Rule Selection 400 Interpreting the Results 401 Rules and Chance 403 Example 2: Rules for Similar Book Purchases 405 15.2 Collaborative Filtering 407 Data Type and Format 407 Example 3: Netflix Prize Contest 408 User-Based Collaborative Filtering: "People Like You" 409 Item-Based Collaborative Filtering 411 Evaluating Performance 412 Example 4: Predicting Movie Ratings with MovieLens Data 413 Advantages and Weaknesses of Collaborative Filtering 416 Collaborative Filtering vs. Association Rules 417 15.3 Summary 419 Problems 421 Chapter 16 Cluster Analysis 425 16.1 Introduction 426 Example: Public Utilities 427 16.2 Measuring Distance Between Two Records 429 Euclidean Distance 429 Normalizing Numerical Variables 430 Other Distance Measures for Numerical Data 432 Distance Measures for Categorical Data 433 Distance Measures for Mixed Data 434 16.3 Measuring Distance Between Two Clusters 434 Minimum Distance 434 Maximum Distance 435 Average Distance 435 Centroid Distance 435 16.4 Hierarchical (Agglomerative) Clustering 437 Single Linkage 437 Complete Linkage 438 Average Linkage 438 Centroid Linkage 438 Ward's Method 438 Dendrograms: Displaying Clustering Process and Results 439 Validating Clusters 441 Limitations of Hierarchical Clustering 443 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 444 Choosing the Number of Clusters (k) 445 Problems 450 Part VII Forecasting Time Series Chapter 17 Handling Time Series 455 17.1 Introduction 455 17.2 Descriptive vs. Predictive Modeling 457 17.3 Popular Forecasting Methods in Business 457 Problems 466 Chapter 18 Regression-Based Forecasting 469 18.1 A Model with Trend 469 Linear Trend 469 Exponential Trend 473 Polynomial Trend 474 Problems 489 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 499 19.1 Smoothing Methods: Introduction 500 19.2 Moving Average 500 Centered Moving Average for Visualization 500 Trailing Moving Average for Forecasting 501 Choosing Window Width (w) 504 Problems 516 Part VIII Data Analytics Chapter 20 Social Network Analytics 527 20.1 Introduction 527 20.2 Directed vs. Undirected Networks 529 20.3 Visualizing and Analyzing Networks 530 Plot Layout 530 Edge List 533 Adjacency Matrix 533 Using Network Data in Classification and Prediction 534 Problems 548 Chapter 21 Text Mining 549 21.1 Introduction 549 21.2 The Tabular Representation of Text 550 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 551 Problems 570 Chapter 22 Responsible Data Science 573 22.1 Introduction 573 22.2 Unintentional Harm 574 22.3 Legal Considerations 576 22.4 Principles of Responsible Data Science 577 Non-maleficence 578 Fairness 578 Transparency 579 Accountability 580 Data Privacy and Security 580 Problems 599 Part IX Cases Chapter 23 Cases 603 23.1 Charles Book Club 603 The Book Industry 603 Database Marketing at Charles 604 Machine Learning Techniques 606 Assignment 608 23.2 German Credit 610 Background 610 Data 610 Assignment 614 Index 647