Efnisyfirlit

• Contents
• Foreword
• Preface to the Revised and Updated Edition
  • Frequently Asked Questions about Predictive Analytics
    • Who Is this Book for?
    • Is the Idea of predictive analytics Hard to Understand?
    • Is this Book a How-To?
    • Not a How-To? Then Why Should Techies Read it?
    • What Is the Purpose of this Book?
    • How Technical Does this Book Get?
    • Is this a University Textbook?
    • How Should I Read this Book?
    • What's New in the ``Revised and Updated´´ Edition of Predictive Analytics?
    • Where Can I Learn More After this Book, Such as a How-To for Hands-On Practice?
• Preface to the Original Edition
• Introduction
  • Prediction in Big Business-The Destiny of Assets
  • Introducing . . . the Clairvoyant Computer
  • ``Feed Me!´´-Food for Thought for the Machine
  • I Knew You Were Going to Do That
  • The Limits and Potential of Prediction
  • The Field of Dreams
  • Organizational Learning
  • The New Super Geek: Data Scientists
  • The Art of Learning
• Chapter 1: Liftoff! Prediction Takes Action
  • Going Live
  • A Faulty Oracle Everyone Loves
  • Predictive Protection
  • A Silent Revolution Worth a Million
  • The Perils of Personalization
  • Deployment's Detours and Delays
  • In Flight
  • Elementary, My Dear: The Power of Observation
  • To Act Is to Decide
  • A Perilous Launch
  • Houston, We Have a Problem
  • The Little Model That Could
  • Houston, We Have Liftoff
  • A Passionate Scientist
  • Launching Prediction into Inner Space
• Chapter 2: With Power Comes Responsibility
  • The Prediction of Target and the Target of Prediction
  • A Pregnant Pause
  • My 15 Minutes
  • Thrust into the Limelight
  • You Can't Imprison Something That Can Teleport
  • Law and Order: Policies and Policing of Data
  • The Battle over Data
  • Data Mining Does Not Drill Down
  • HP Learns about Itself
  • Insight or Intrusion?
  • Flight Risk: I Quit!
  • Insights: The Factors behind Quitting
  • Delivering Dynamite
  • The Value Gained from Flight Risk
  • Predicting Crime to Stop It Before It Happens
  • The Data of Crime and the Crime of Data
  • Machine Risk without Measure
  • The Cyclicity of Prejudice
  • Good Prediction, Bad Prediction
  • The Source of Power
• Chapter 3: The Data Effect
  • A Cautionary Tale: Orange Lemons
  • The Source: Otherwise Boring Logs Fuel Prediction
  • Social Media and Mass Public Mood
  • Recycling the Data Dump
  • The Instrumentation of Everything We Do
  • Batten Down the Hatches: TMI
  • Who's Your Data?
  • The Data Effect: It's Predictive
  • The Building Blocks: Predictors
  • Far Out, Bizarre, and Surprising Insights
  • Caveat #1: Correlation Does Not Imply Causation
  • Caveat #2: Securing Sound Discoveries
  • What Went Wrong: Accumulating Risk
  • The Potential and Danger of Automating Science: Vast Search
  • A Failsafe for Sound Results
  • A Prevalent Mistake
  • Putting All the Predictors Together
• Chapter 4: The Machine That Learns
  • Boy Meets Bank
  • Bank Faces Risk
  • Prediction Battles Risk
  • Risky Business
  • The Learning Machine
  • Building the Learning Machine
  • Learning from Bad Experiences
  • How Machine Learning Works
  • Decision Trees Grow on You
  • Computer, Program Thyself
  • Learn Baby Learn
  • Bigger Is Better
  • Overlearning: Assuming Too Much
  • The Conundrum of Induction
  • The Art and Science of Machine Learning
  • Feeling Validated: Test Data
  • Carving out a Work of Art
  • Putting Decision Trees to Work for Chase
  • Money Grows on Trees
  • The Recession-Why Microscopes Can't Detect Asteroid Collisions
  • After Math
• Chapter 5: The Ensemble Effect
  • Casual Rocket Scientists
  • Dark Horses
  • Mindsourced: Wealth in Diversity
  • Crowdsourcing Gone Wild
  • Your Adversary Is Your Amigo
  • United Nations
  • Meta-Learning
  • A Big Fish at the Big Finish
  • Collective Intelligence
  • The Wisdom of Crowds . . . of Models
  • A Bag of Models
  • Ensemble Models in Action
  • The Generalization Paradox: More Is Less
  • The Sky's the Limit
• Chapter 6: Watson and the Jeopardy! Challenge
  • Text Analytics
  • Our Mother Tongue's Trials and Tribulations
  • Once You Understand the Question, Answer It
  • The Ultimate Knowledge Source
  • Artificial Impossibility
  • Learning to Answer Questions
  • Walk Like a Man, Talk Like a Man
  • Putting on the Pressure
  • The Answering Machine
  • Moneyballing Jeopardy!
  • Amassing Evidence for an Answer
  • Elementary, My Dear Watson
  • Mounting Evidence
  • Weighing Evidence with Ensemble Models
  • An Ensemble of Ensembles
  • Machine Learning Achieves the Potential of Natural Language Processing
  • Confidence without Overconfidence
  • The Need for Speed
  • Double Jeopardy!-Would Watson Win?
  • Jeopardy! Jitters: Deploying a Prototype
  • For the Win
  • After Match: Honor, Accolades, and Awe
  • Iambic IBM AI
  • Predict the Right Thing
• Chapter 7: Persuasion by the Numbers
  • Churn Baby Churn
  • Sleeping Dogs
  • A New Thing to Predict
  • Eye Can't See It
  • Perceiving Persuasion
  • Persuasive Choices
  • Business Stimulus and Business Response
  • The Quantum Human
  • Predicting Influence with Uplift Modeling
  • Banking on Influence
  • Predicting the Wrong Thing
  • Response Uplift Modeling
  • The Mechanics of Uplift Modeling
  • How Uplift Modeling Works
  • The Persuasion Effect
  • Influence across Industries
  • Immobilizing Mobile Customers
• Afterword
  • Tomorrow's Just a Day Away
  • The Future of Prediction
• Appendix A: The Five Effects of Prediction
• Appendix B: Twenty Applications of Predictive Analytics
• Appendix C: Prediction People-Cast of ``Characters´´
• Hands-On Guide
• Acknowledgments
• About the Author
• 182 Examples of Predictive Analytics
• Index
• End User License Agreement

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