Machine learning for business analytics : concepts, techniques and applications with JMP Pro®

xxiv, 584 Seiten

Illustrationen, Diagramme

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 Statistical Modeling vs. Machine Learning 6 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 Order of Topics 12 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 19 2.3 The Steps in A Machine Learning Project 21 2.4 Preliminary Steps 22 Organization of Data 22 Sampling from a Database 22 Oversampling Rare Events in Classification Tasks 23 Preprocessing and Cleaning the Data 23 2.5 Predictive Power and Overfitting 29 Overfitting 29 Creation and Use of Data Partitions 31 2.6 Building a Predictive Model with JMP Pro 34 Predicting Home Values in a Boston Neighborhood 34 Modeling Process 36 2.7 Using JMP Pro for Machine Learning 42 2.8 Automating Machine Learning Solutions 43 Predicting Power Generator Failure 44 Uber's Michelangelo 45 2.9 Ethical Practice in Machine Learning 47 Machine Learning Software: The State of the Market by Herb Edelstein 47 Problems 52 PART II DATA EXPLORATION AND DIMENSION REDUCTION 3 Data Visualization 59 3.1 Introduction 59 3.2 Data Examples 61 Example 1: Boston Housing Data 61 Example 2: Ridership on Amtrak Trains 62 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62 Distribution Plots: Boxplots and Histograms 64 Heatmaps 67 3.4 Multidimensional Visualization 70 Adding Variables: Color, Hue, Size, Shape, Multiple Panels, Animation 70 Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 73 Reference: Trend Line and Labels 77 Scaling Up: Large Datasets 79 Multivariate Plot: Parallel Coordinates Plot 80 Interactive Visualization 80 3.5 Specialized Visualizations 82 Visualizing Networked Data 82 Visualizing Hierarchical Data: More on Treemaps 83 Visualizing Geographical Data: Maps 84 3.6 Summary: Major Visualizations and Operations, According to Machine Learning Goal 87 Prediction 87 Classification 87 Time Series Forecasting 87 Unsupervised Learning 88 Problems 89 4 Dimension Reduction 91 4.1 Introduction 91 4.2 Curse of Dimensionality 92 4.3 Practical Considerations 92 Example 1: House Prices in Boston 92 4.4 Data Summaries 93 Summary Statistics 94 Tabulating Data 96 4.5 Correlation Analysis 97 4.6 Reducing the Number of Categories in Categorical Variables 98 4.7 Converting a Categorical Variable to a Continuous Variable 100 4.8 Principal Component Analysis 100 Example 2: Breakfast Cereals 101 Principal Components 106 Standardizing the Data 107 Using Principal Components for Classification and Prediction 110 4.9 Dimension Reduction Using Regression Models 110 4.10 Dimension Reduction Using Classification and Regression Trees 111 Problems 112 PART III PERFORMANCE EVALUATION 5 Evaluating Predictive Performance 117 5.1 Introduction 118 5.2 Evaluating Predictive Performance 118 Naive Benchmark: The Average 118 Prediction Accuracy Measures 119 Comparing Training and Validation Performance 120 5.3 Judging Classifier Performance 121 Benchmark: The Naive Rule 121 Class Separation 121 The Classification (Confusion) Matrix 122 Using the V

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