Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner

• Autore: Ratner Bruce
• Pubblicazione: Boca Raton : , : Taylor & Francis, , 2012
• Edizione: 2nd ed.
• Descrizione fisica: 1 online resource (524 p.)
• Disciplina: 658.8/72
• Soggetto topico: Database marketing - Statistical methods; Data mining - Statistical methods
• Soggetto genere / forma: Electronic books.
• Altri autori: RatnerBruce
• Note generali: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They? ; 8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression ; 16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison ; 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models
• Sommario/riassunto: The second 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 first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has
• Titolo autorizzato: Statistical and machine-learning data mining
• ISBN: 0-429-24862-8; 1-4665-5121-6; 1-280-12244-7; 9786613526304; 1-4398-6092-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910457262503321
• Lo trovi qui: Univ. Federico II