LEADER 04869oam 2200709I 450 001 9910457262503321 005 20200520144314.0 010 $a0-429-24862-8 010 $a1-4665-5121-6 010 $a1-280-12244-7 010 $a9786613526304 010 $a1-4398-6092-0 024 7 $a10.1201/b11508 035 $a(CKB)2550000000079683 035 $a(EBL)840391 035 $a(OCoLC)774956267 035 $a(SSID)ssj0000600024 035 $a(PQKBManifestationID)11427987 035 $a(PQKBTitleCode)TC0000600024 035 $a(PQKBWorkID)10617282 035 $a(PQKB)10410659 035 $a(MiAaPQ)EBC840391 035 $a(PPN)175662886 035 $a(Au-PeEL)EBL840391 035 $a(CaPaEBR)ebr10524974 035 $a(CaONFJC)MIL352630 035 $a(OCoLC)778434844 035 $a(EXLCZ)992550000000079683 100 $a20180331d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical and machine-learning data mining $etechniques for better predictive modeling and analysis of big data /$fBruce Ratner 205 $a2nd ed. 210 1$aBoca Raton :$cTaylor & Francis,$d2012. 215 $a1 online resource (524 p.) 300 $aRev. ed. of: Statistical modeling and analysis for database marketing. c2003. 311 $a1-4398-6091-2 320 $aIncludes bibliographical references and index. 327 $aFront 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? 327 $a8. 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 327 $a16. 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 327 $a25. 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 330 $aThe 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 606 $aDatabase marketing$xStatistical methods 606 $aData mining$xStatistical methods 608 $aElectronic books. 615 0$aDatabase marketing$xStatistical methods. 615 0$aData mining$xStatistical methods. 676 $a658.8/72 700 $aRatner$b Bruce. 701 $aRatner$b Bruce$0283304 701 $aRatner$b Bruce$0283304 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910457262503321 996 $aStatistical and machine-learning data mining$92008772 997 $aUNINA