Machine Learning for Business Analytics - Galit Shmueli

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Résumé :

Foreword by Gareth James xxi

Preface to the Second Python 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? 8

1.7 Terminology and Notation 9

1.8 Road Maps to This Book 12

Order of Topics 13

Chapter 2 Overview of the Machine Learning Process 17

2.1 Introduction 18

2.2 Core Ideas in Machine Learning 18

2.3 The Steps in a Machine Learning Project 22

2.4 Preliminary Steps 23

2.5 Predictive Power and Overfitting 37

2.6 Building a Predictive Model 43

2.7 Using Python for Machine Learning on a Local Machine 49

2.8 Automating Machine Learning Solutions 49

2.9 Ethical Practice in Machine Learning 54

Problems 55

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization 61

3.1 Uses of Data Visualization 62

3.2 Data Examples 64

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

3.4 Multidimensional Visualization 75

3.5 Specialized Visualizations 90

Problems 98

Chapter 4 Dimension Reduction 101

4.1 Introduction 102

4.2 Curse of Dimensionality 102

4.3 Practical Considerations 103

4.4 Data Summaries 103

4.5 Correlation Analysis 108

4.6 Reducing the Number of Categories in Categorical Variables 109

4.7 Converting a Categorical Variable to a Numerical Variable 109

4.8 Principal Component Analysis 111

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 131

5.3 Judging Classifier Performance 137

5.4 Judging Ranking Performance 150

5.5 Oversampling 156

Problems 162

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression 167

6.1 Introduction 168

6.2 Explanatory vs. Predictive Modeling 168

6.3 Estimating the Regression Equation and Prediction 170

6.4 Variable Selection in Linear Regression 176

Problems 188

Chapter 7 k-Nearest Neighbors (k-NN) 193

7.1 The k-NN Classifier (Categorical Outcome) 194

7.2 k-NN for a Numerical Outcome 203

7.3 Advantages and Shortcomings of k-NN Algorithms 205

Problems 207

Chapter 8 The Naive Bayes Classifier 209

8.1 Introduction 209

8.2 Applying the Full (Exact) Bayesian Classifier 212

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 230

9.2 Classification Trees 232

9.3 Evaluating the Performance of a Classification Tree 241

9.4 Avoiding Overfitting 246

9.5 Classification Rules from Trees 252

9.6 Classification Trees for More Than Two Classes 252

9.7 Regression Trees 253

9.8 Advantages and Weaknesses of a Tree 256

9.9 Improving Prediction: Random Forests and Boosted Trees 258

Problems 264

Chapter 10 Logistic Regression 267

10.1 Introduction 268

10.2 The Logistic Regression Model 269

10.3 Example: Acceptance of Personal Loa...

Sommaire:

Foreword by Gareth James xxi
Preface to the Second Python 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? 8
1.7 Terminology and Notation 9
1.8 Road Maps to This Book 12

Chapter 2 Overview of the Machine Learning Process 17
2.1 Introduction 18
2.2 Core Ideas in Machine Learning 18
2.3 The Steps in a Machine Learning Project 22
2.4 Preliminary Steps 23
2.5 Predictive Power and Overfitting 37
2.6 Building a Predictive Model 43
2.7 Using Python for Machine Learning on a Local Machine 49
2.8 Automating Machine Learning Solutions 49
2.9 Ethical Practice in Machine Learning 54

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization 61
3.1 Uses of Data Visualization 62
3.2 Data Examples 64
3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66
3.4 Multidimensional Visualization 75
3.5 Specialized Visualizations 90

Chapter 4 Dimension Reduction 101
4.1 Introduction 102
4.2 Curse of Dimensionality 102
4.3 Practical Considerations 103
4.4 Data Summaries 103
4.5 Correlation Analysis 108
4.6 Reducing the Number of Categories in Categorical Variables 109
4.7 Converting a Categorical Variable to a Numerical Variable 109
4.8 Principal Component Analysis 111
4.9 Dimension Reduction Using Regression Models 121
4.10 Dimension Reduction Using Classification and Regression Trees 121

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance 129
5.1 Introduction 130
5.2 Evaluating Predictive Performance 131
5.3 Judging Classifier Performance 137
5.4 Judging Ranking Performance 150
5.5 Oversampling 156

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression 167
6.1 Introduction 168
6.2 Explanatory vs. Predictive Modeling 168
6.3 Estimating the Regression Equation and Prediction 170
6.4 Variable Selection in Linear Regression 176

Chapter 7 k-Nearest Neighbors (k-NN) 193
7.1 The k-NN Classifier (Categorical Outcome) 194
7.2 k-NN for a Numerical Outcome 203
7.3 Advantages and Shortcomings of k-NN Algorithms 205

Chapter 8 The Naive Bayes Classifier 209
8.1 Introduction 209
8.2 Applying the Full (Exact) Bayesian Classifier 212
8.3 Solution: Naive Bayes 213
8.4 Advantages and Shortcomings of the Naive Bayes Classifier 224

Chapter 9 Classification and Regression Trees 229
9.1 Introduction 230
9.2 Classification Trees 232
9.3 Evaluating the Performance of a Classification Tree 241
9.4 Avoiding Overfitting 246
9.5 Classification Rules from Trees 252
9.6 Classification Trees for More Than Two Classes 252
9.7 Regression Trees 253
9.8 Advantages and Weaknesses of a Tree 256
9.9 Improving Prediction: Random Forests and Boosted Trees 258

Chapter 10 Logistic Regression 267
10.1 Introduction 268
10.2 The Logistic Regression Model 269
10.3 Example: Acceptance of Personal Loan 272
10.4 Evaluating Classification Performance 277
10.5 Variable Selection 280
10.6 Logistic Regression for Multi-Class Classification 281
10.7 Example of Complete Analysis: Predicting Delayed Flights 285

Chapter 11 Neural Nets 301
11.1 Introduction 302
11.2 Concept and Structure of a Neural Network 302
11.3 Fitting a Networ...