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The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
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Autorenporträt
Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals - across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.
Inhaltsangabe
Preface to Third Edition Preface of Second Edition Acknowledgments Author 1. Introduction 2. Science Dealing with Data: Statistics and Data Science 3. Two Basic Data Mining Methods for Variable Assessment 4. CHAID-Based Data Mining for Paired-Variable Assessment 5. The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice 6. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data 7. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment 8. Market Share Estimation: Data Mining for an Exceptional Case 9. The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They? 10. Logistic Regression: The Workhorse of Response Modeling 11. Predicting Share of Wallet without Survey Data 12. Ordinary Regression: The Workhorse of Profit Modeling 13. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution 14. CHAID for Interpreting a Logistic Regression Model 15. The Importance of the Regression Coefficient 16. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables 17. CHAID for Specifying a Model with Interaction Variables 18. Market Segmentation Classification Modeling with Logistic Regression 19. Market Segmentation Based on Time-Series Data Using Latent Class Analysis 20. Market Segmentation: An Easy Way to Understand the Segments 21. The Statistical Regression Model: An Easy Way to Understand the Model 22. CHAID as a Method for Filling in Missing Values 23. Model Building with Big Complete and Incomplete Data 24. Art, Science, Numbers, and Poetry 25. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling 26. Assessment of Marketing Models 27. Decile Analysis: Perspective and Performance 28. Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns 29. Bootstrapping in Marketing: A New Approach for Validating Models 30. Validating the Logistic Regression Model: Try Bootstrapping 31. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model 32. The Predictive Contribution Coefficient: A Measure of Predictive Importance 33. Regression Modeling Involves Art, Science, and Poetry, Too 34. Opening the Dataset: A Twelve-Step Program for Dataholics 35. Genetic and Statistic Regression Models: A Comparison 36. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model 37. A Data Mining Method for Moderating Outliers Instead of Discarding Them 38. Overfitting: Old Problem, New Solution 39. The Importance of Straight Data: Revisited 40. The GenIQ Model: Its Definition and an Application 41. Finding the Best Variables for Marketing Models 42. Interpretation of Coefficient-Free Models 43. Text Mining: Primer, Illustration, and TXTDM Software 44. Some of My Favorite Statistical Subroutines Index
Preface to Third Edition Preface of Second Edition Acknowledgments Author 1. Introduction 2. Science Dealing with Data: Statistics and Data Science 3. Two Basic Data Mining Methods for Variable Assessment 4. CHAID-Based Data Mining for Paired-Variable Assessment 5. The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice 6. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data 7. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment 8. Market Share Estimation: Data Mining for an Exceptional Case 9. The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They? 10. Logistic Regression: The Workhorse of Response Modeling 11. Predicting Share of Wallet without Survey Data 12. Ordinary Regression: The Workhorse of Profit Modeling 13. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution 14. CHAID for Interpreting a Logistic Regression Model 15. The Importance of the Regression Coefficient 16. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables 17. CHAID for Specifying a Model with Interaction Variables 18. Market Segmentation Classification Modeling with Logistic Regression 19. Market Segmentation Based on Time-Series Data Using Latent Class Analysis 20. Market Segmentation: An Easy Way to Understand the Segments 21. The Statistical Regression Model: An Easy Way to Understand the Model 22. CHAID as a Method for Filling in Missing Values 23. Model Building with Big Complete and Incomplete Data 24. Art, Science, Numbers, and Poetry 25. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling 26. Assessment of Marketing Models 27. Decile Analysis: Perspective and Performance 28. Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns 29. Bootstrapping in Marketing: A New Approach for Validating Models 30. Validating the Logistic Regression Model: Try Bootstrapping 31. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model 32. The Predictive Contribution Coefficient: A Measure of Predictive Importance 33. Regression Modeling Involves Art, Science, and Poetry, Too 34. Opening the Dataset: A Twelve-Step Program for Dataholics 35. Genetic and Statistic Regression Models: A Comparison 36. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model 37. A Data Mining Method for Moderating Outliers Instead of Discarding Them 38. Overfitting: Old Problem, New Solution 39. The Importance of Straight Data: Revisited 40. The GenIQ Model: Its Definition and an Application 41. Finding the Best Variables for Marketing Models 42. Interpretation of Coefficient-Free Models 43. Text Mining: Primer, Illustration, and TXTDM Software 44. Some of My Favorite Statistical Subroutines Index
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