Machine Learning for Business Analytics - Galit Shmueli

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Résumé :

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 Adv...

Biographie:
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(r) and JMP Pro(r). Muralidhara Anandamurthy, PhD is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro(r). 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.

Sommaire:

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.