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    Copyright
    Contents
    Preface
    Structure of the Book
    How to Use this Book
    Online Resources
    Acknowledgements
    Notation
    Notational Conventions
    Notational Conventions for Probabilities
    1 Machine Learning for Predictive Data Analytics
    1.1 What Is Predictive Data Analytics?
    1.2 What Is Machine Learning?
    1.3 How Does Machine Learning Work?
    1.4 What Can Go Wrong with Machine Learning?
    1.5 The Predictive Data Analytics Project Lifecycle: CRISP-DM
    1.6 Predictive Data Analytics Tools
    1.7 The Road Ahead
    1.8 Exercises
    2 Data to Insights to Decisions
    2.1 Converting Business Problems into Analytics Solutions
    2.1.1 Case Study: Motor Insurance Fraud
    2.2 Assessing Feasibility
    2.2.1 Case Study: Motor Insurance Fraud
    2.3 Designing the Analytics Base Table
    2.3.1 Case Study: Motor Insurance Fraud
    2.4 Designing and Implementing Features
    2.4.1 Different Types of Data
    2.4.2 Different Types of Features
    2.4.3 Handling Time
    2.4.4 Legal Issues
    2.4.5 Implementing Features
    2.4.6 Case Study: Motor Insurance Fraud
    2.5 Summary
    2.6 Further Reading
    2.7 Exercises
    3 Data Exploration
    3.1 The Data Quality Report
    3.1.1 Case Study: Motor Insurance Fraud
    3.2 Getting to Know the Data
    3.2.1 The Normal Distribution
    3.2.2 Case Study: Motor Insurance Fraud
    3.3 Identifying Data Quality Issues
    3.3.1 Missing Values
    3.3.2 Irregular Cardinality
    3.3.3 Outliers
    3.3.4 Case Study: Motor Insurance Fraud
    3.4 Handling Data Quality Issues
    3.4.1 Handling Missing Values
    3.4.2 Handling Outliers
    3.4.3 Case Study: Motor Insurance Fraud
    3.5 Advanced Data Exploration
    3.5.1 Visualizing Relationships Between Features
    3.5.2 Measuring Covariance and Correlation
    3.6 Data Preparation
    3.6.1 Normalization
    3.6.2 Binning
    3.6.3 Sampling
    3.7 Summary
    3.8 Further Reading
    3.9 Exercises
    4 Information-based Learning
    4.1 Big Idea
    4.2 Fundamentals
    4.2.1 Decision Trees
    4.2.2 Shannon’s Entropy Model
    4.2.3 Information Gain
    4.3 Standard Approach: The ID3 Algorithm
    4.3.1 A Worked Example: Predicting Vegetation Distributions
    4.4 Extensions and Variations
    4.4.1 Alternative Feature Selection and Impurity Metrics
    4.4.2 Handling Continuous Descriptive Features
    4.4.3 Predicting Continuous Targets
    4.4.4 Tree Pruning
    4.4.5 Model Ensembles
    4.5 Summary
    4.6 Further Reading
    4.7 Exercises
    5 Similarity-based Learning
    5.1 Big Idea
    5.2 Fundamentals
    5.2.1 Feature Space
    5.2.2 Measuring Similarity Using Distance Metrics
    5.3 Standard Approach: The Nearest Neighbor Algorithm
    5.3.1 A Worked Example
    5.4 Extensions and Variations
    5.4.1 Handling Noisy Data
    5.4.2 Efficient Memory Search
    5.4.3 Data Normalization
    5.4.4 Predicting Continuous Targets
    5.4.5 Other Measures of Similarity
    5.4.6 Feature Selection
    5.5 Summary
    5.6 Further Reading
    5.7 Epilogue
    5.8 Exercises
    6 Probability-based Learning
    6.1 Big Idea
    6.2 Fundamentals
    6.2.1 Bayes’ Theorem
    6.2.2 Bayesian Prediction
    6.2.3 Conditional Independence and Factorization
    6.3 Standard Approach: The Naive Bayes Model
    6.3.1 A Worked Example
    6.4 Extensions and Variations
    6.4.1 Smoothing
    6.4.2 Continuous Features: Probability Density Functions
    6.4.3 Continuous Features: Binning
    6.4.4 Bayesian Networks
    6.5 Summary
    6.6 Further Reading
    6.7 Exercises
    7 Error-based Learning
    7.1 Big Idea
    7.2 Fundamentals
    7.2.1 Simple Linear Regression
    7.2.2 Measuring Error
    7.2.3 Error Surfaces
    7.3 Standard Approach: Multivariable Linear Regression with Gradient Descent
    7.3.1 Multivariable Linear Regression
    7.3.2 Gradient Descent
    7.3.3 Choosing Learning Rates and Initial Weights
    7.3.4 A Worked Example
    7.4 Extensions and Variations
    7.4.1 Interpreting Multivariable Linear Regression Models
    7.4.2 Setting the Learning Rate Using Weight Decay
    7.4.3 Handling Categorical Descriptive Features
    7.4.4 Handling Categorical Target Features: Logistic Regression
    7.4.5 Modeling Non-linear Relationships
    7.4.6 Multinomial Logistic Regression
    7.4.7 Support Vector Machines
    7.5 Summary
    7.6 Further Reading
    7.7 Exercises
    8 Evaluation
    8.1 Big Idea
    8.2 Fundamentals
    8.3 Standard Approach: Misclassification Rate on a Hold-out Test Set
    8.4 Extensions and Variations
    8.4.1 Designing Evaluation Experiments
    8.4.2 Performance Measures: Categorical Targets
    8.4.3 Performance Measures: Prediction Scores
    8.4.4 Performance Measures: Multinomial Targets
    8.4.5 Performance Measures: Continuous Targets
    8.4.6 Evaluating Models after Deployment
    8.5 Summary
    8.6 Further Reading
    8.7 Exercises
    9 Case Study: Customer Churn
    9.1 Business Understanding
    9.2 Data Understanding
    9.3 Data Preparation
    9.4 Modeling
    9.5 Evaluation
    9.6 Deployment
    10 Case Study: Galaxy Classification
    10.1 Business Understanding
    10.1.1 Situational Fluency
    10.2 Data Understanding
    10.3 Data Preparation
    10.4 Modeling
    10.4.1 Baseline Models
    10.4.2 Feature Selection
    10.4.3 The 5-level Model
    10.5 Evaluation
    10.6 Deployment
    11 The Art of Machine Learning for Predictive Data Analytics
    11.1 Different Perspectives on Prediction Models
    11.2 Choosing a Machine Learning Approach
    11.2.1 Matching Machine Learning Approaches to Projects
    11.2.2 Matching Machine Learning Approaches to Data
    11.3 Your Next Steps
    A Descriptive Statistics and Data Visualization for Machine Learning
    A.1 Descriptive Statistics for Continuous Features
    A.1.1 Central Tendency
    A.1.2 Variation
    A.2 Descriptive Statistics for Categorical Features
    A.3 Populations and Samples
    A.4 Data Visualization
    A.4.1 Bar Plots
    A.4.2 Histograms
    A.4.3 Box Plots
    B Introduction to Probability for Machine Learning
    B.1 Probability Basics
    B.2 Probability Distributions and Summing Out
    B.3 Some Useful Probability Rules
    B.4 Summary
    C Differentiation Techniques for Machine Learning
    C.1 Derivatives of Continuous Functions
    C.2 The Chain Rule
    C.3 Partial Derivatives
    Bibliography
    List of Figures
    List of Tables
    Index
    FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS
    FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS Algorithms, Worked Examples, and Case Studies John D. Kelleher Brian Mac Namee Aoife D’Arcy The MIT Press Cambridge, Massachusetts London, England
    © 2015 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please email special_sales@mitpress.mit.edu This book was set in the the United States of America. Library of Congress Cataloging-in-Publication Data Kelleher, John D., 1974 Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / John D. Kelleher, Brian Mac Namee, and Aoife DArcy. programming language by the author. Printed and bound in pages cm Includes bibliographical references and index. ISBN 978-0-262-02944-5 (hardcover : alk. paper) 1. Machine learning. 2. Data mining. 3. Prediction theory. I. Mac Namee, Brian, 1978 II. DArcy, Aoife, 1978 III. Title. Q325.5.K455 2015 006.3’1—dc23 10 9 8 7 6 5 4 3 2 1 2014046123
    To my wife and family, thank you for your love, support, and patience. John To my family. Brian To Grandad D’Arcy, for the inspiration. Aoife
    Contents Copyright Preface Structure of the Book How to Use this Book Online Resources Acknowledgements Notation Notational Conventions Notational Conventions for Probabilities 1 Machine Learning for Predictive Data Analytics 1.1 What Is Predictive Data Analytics? 1.2 What Is Machine Learning? 1.3 How Does Machine Learning Work? 1.4 What Can Go Wrong with Machine Learning? 1.5 The Predictive Data Analytics Project Lifecycle: CRISP-DM 1.6 Predictive Data Analytics Tools 1.7 The Road Ahead 1.8 Exercises 2 Data to Insights to Decisions 2.1 Converting Business Problems into Analytics Solutions 2.1.1 Case Study: Motor Insurance Fraud 2.2 Assessing Feasibility 2.2.1 Case Study: Motor Insurance Fraud 2.3 Designing the Analytics Base Table 2.3.1 Case Study: Motor Insurance Fraud 2.4 Designing and Implementing Features 2.4.1 Different Types of Data
    2.4.2 Different Types of Features 2.4.3 Handling Time 2.4.4 Legal Issues 2.4.5 Implementing Features 2.4.6 Case Study: Motor Insurance Fraud 2.5 Summary 2.6 Further Reading 2.7 Exercises 3 Data Exploration 3.1 The Data Quality Report 3.1.1 Case Study: Motor Insurance Fraud 3.2 Getting to Know the Data 3.2.1 The Normal Distribution 3.2.2 Case Study: Motor Insurance Fraud 3.3 Identifying Data Quality Issues 3.3.1 Missing Values 3.3.2 Irregular Cardinality 3.3.3 Outliers 3.3.4 Case Study: Motor Insurance Fraud 3.4 Handling Data Quality Issues 3.4.1 Handling Missing Values 3.4.2 Handling Outliers 3.4.3 Case Study: Motor Insurance Fraud 3.5 Advanced Data Exploration 3.5.1 Visualizing Relationships Between Features 3.5.2 Measuring Covariance and Correlation 3.6 Data Preparation 3.6.1 Normalization 3.6.2 Binning 3.6.3 Sampling 3.7 Summary
    3.8 Further Reading 3.9 Exercises 4 Information-based Learning 4.1 Big Idea 4.2 Fundamentals 4.2.1 Decision Trees 4.2.2 Shannon’s Entropy Model 4.2.3 Information Gain 4.3 Standard Approach: The ID3 Algorithm 4.3.1 A Worked Example: Predicting Vegetation Distributions 4.4 Extensions and Variations 4.4.1 Alternative Feature Selection and Impurity Metrics 4.4.2 Handling Continuous Descriptive Features 4.4.3 Predicting Continuous Targets 4.4.4 Tree Pruning 4.4.5 Model Ensembles 4.5 Summary 4.6 Further Reading 4.7 Exercises 5 Similarity-based Learning 5.1 Big Idea 5.2 Fundamentals 5.2.1 Feature Space 5.2.2 Measuring Similarity Using Distance Metrics 5.3 Standard Approach: The Nearest Neighbor Algorithm 5.3.1 A Worked Example 5.4 Extensions and Variations 5.4.1 Handling Noisy Data 5.4.2 Efficient Memory Search 5.4.3 Data Normalization 5.4.4 Predicting Continuous Targets
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