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An up-to-date introduction to a market-leading platform for data analysis and machine learning
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find:
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.
Galit Shmueli, PhD is Distinguished Professor 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.
Mia L. Stephens, M.S. is an Advisory Product Manager with JMP, driving the product vision and roadmaps for JMP and JMP Pro.
Muralidhara Anandamurthy, PhD is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro.
Nitin R. Patel, PhD is cofounder and lead researcher at Cytel Inc. He is also a Fellow of the American Statistical Association and has served as a visiting professor at the Massachusetts Institute of Technology and Harvard University, among others.
Foreword xix
Preface xx
Acknowledgments xxiii
Part I Preliminaries
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 6
1.5 Data Science 7
1.6 Why Are There So Many Different Methods? 8
1.7 Terminology and Notation 8
1.8 Road Maps to This Book 10
2 Overview of the Machine Learning Process 17
2.1 Introduction 17
2.2 Core Ideas in Machine Learning 18
2.3 The Steps in A Machine Learning Project 21
2.4 Preliminary Steps 22
2.5 Predictive Power and Overfitting 29
2.6 Building a Predictive Model with JMP Pro 34
2.7 Using JMP Pro for Machine Learning 42
2.8 Automating Machine Learning Solutions 43
2.9 Ethical Practice in Machine Learning 47
Part II Data Exploration and Dimension Reduction
3 Data Visualization 59
3.1 Introduction 59
3.2 Data Examples 61
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62
3.4 Multidimensional Visualization 70
3.5 Specialized Visualizations 82
3.6 Summary: Major Visualizations and Operations, According to Machine Learning Goal 87
4 Dimension Reduction 91
4.1 Introduction 91
4.2 Curse of Dimensionality 92
4.3 Practical Considerations 92
Part III Performance Evaluation
5 Evaluating Predictive Performance 117
5.1 Introduction 118
5.2 Evaluating Predictive Performance 118
Part IV Prediction and Classification Methods
6 Multiple Linear Regression 147
6.1 Introduction 147
6.2 Explanatory vs. Predictive Modeling 148
6.3 Estimating the Regression Equation and Prediction 149
6.4 Variable Selection in Linear Regression 155
7 k-Nearest Neighbors (k-NN) 175
7.1 The k-NN Classifier (Categorical Outcome) 175
8 The Naive Bayes Classifier 189
8.1 Introduction 189
9 Classification and Regression Trees 205
9.1 Introduction 206
9.2 Classification Trees 207
9.3 Growing a Tree for Riding Mowers Example 210
9.4 Evaluating the Performance of a Classification Tree 215
9.5 Avoiding Overfitting 219
9.6 Classification Rules from Trees 222
9.7 Classification Trees for More Than Two Classes 224
9.8 Regression Trees 224
9.9 Advantages and Weaknesses of a Single Tree 227
9.10 Improving Prediction: Random Forests and Boosted Trees 229
10 Logistic Regression 237
10.1 Introduction 237
10.2 The Logistic Regression Model 239
10.3 Example: Acceptance of Personal Loan 240
10.4 Evaluating Classification Performance 247
10.5 Variable Selection 249
10.6 Logistic Regression for Multi-class Classification 250
10.7 Example of Complete Analysis: Predicting Delayed Flights 253
11 Neural Nets 267
11.1 Introduction 267
11.2 Concept and Structure of a Neural Network 268
11.3 Fitting a Network to Data 269
11.4 User Input in JMP Pro 282
11.5 Exploring the Relationship Between Predictors and Outcome 284
11.6 Deep Learning 285
11.7 Advantages and Weaknesses of Neural Networks 289
12 Discriminant Analysis 293
12.1 Introduction 293
12.2 Distance of an Observation from a Class 295
12.3 From Distances to Propensities and Classifications 297
12.4 Classification Performance of Discriminant Analysis 300
12.5 Prior Probabilities 301
12.6 Classifying More Than Two Classes 303
12.7 Advantages and Weaknesses 306
13 Generating, Comparing, and Combining Multiple Models 311
13.1 Ensembles 311
13.2 Automated Machine Learning (AutoML) 317
13.3 Summary 322
Part V Intervention and User Feedback
14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 327
14.1 Introduction 327
14.2 A/B Testing 328
14.3 Uplift (Persuasion) Modeling 333
14.4 Reinforcement Learning 340
14.5 Summary 344
Part VI Mining Relationships Among Records
15 Association Rules and Collaborative Filtering 349
15.1 Association Rules 349
15.2 Collaborative Filtering 362
15.3 Summary 370
16 Cluster Analysis 375
16.1 Introduction 375
16.2 Measuring Distance Between Two Records 378
16.3 Measuring Distance Between Two Clusters 383
16.4 Hierarchical (Agglomerative) Clustering 385
16.5 Nonhierarchical Clustering: The K-Means Algorithm 394
Part VII Forecasting Time Series
17 Handling Time Series 409
17.1 Introduction 409
17.2 Descriptive vs. Predictive Modeling 410
17.3 Popular Forecasting Methods in Business 411
17.4 Time Series Components 411
17.5 Data Partitioning and Performance Evaluation 415
18 Regression-Based Forecasting 423
18.1 A Model with Trend 424
18.2 A Model with Seasonality 430
18.3 A Model with Trend and Seasonality 433
18.4 Autocorrelation and ARIMA Models 433
19 Smoothing and Deep Learning Methods for Forecasting 455
19.1 Introduction 455
19.2 Moving Average 456
19.3 Simple Exponential Smoothing 461
19.4 Advanced Exponential Smoothing 465
19.5 Deep Learning for Forecasting 470
Part VIII Data Analytics
20 Text Mining 483
20.1 Introduction 483
20.2 The Tabular Representation of Text: Document-Term Matrix and "Bag-of-Words" 484
20.3 Bag-of-Words vs. Meaning Extraction at Document Level 486
20.4 Preprocessing the Text 486
20.5 Implementing Machine Learning Methods 492
20.6 Example: Online Discussions on Autos and Electronics 492
20.7 Example: Sentiment Analysis of Movie Reviews 500
20.8 Summary 502
21 Responsible Data Science 505
21.1 Introduction 505
21.2 Unintentional Harm 506
21.3 Legal Considerations 508
21.4 Principles of Responsible Data Science 508
21.5 A Responsible Data Science Framework 511
21.6 Documentation Tools 514
21.7 Example: Applying the RDS Framework to the COMPAS Example 517
21.8 Summary 526
Part IX Cases
22 Cases 533
22.1 Charles Book Club 533
22.2 German Credit 541
22.3 Tayko Software Cataloger 545
22.4 Political Persuasion 548
22.5 Taxi Cancellations 552
22.6 Segmenting Consumers of Bath Soap 554
22.7 Catalog Cross-Selling 557
22.8 Direct-Mail Fundraising 559
22.9 Time Series Case: Forecasting Public Transportation Demand 562
22.10 Loan Approval 564
Index 573
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