MARC Record from marc_columbia

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001 15083552
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035 $a(OCoLC)ocn774956267
035 $a(NNC)15083552
040 $aYDXCP$beng$erda$epn$cYDXCP$dN$T$dEBLCP$dGZM$dMHW$dB24X7$dOCLCQ$dDEBSZ$dGZM$dNLE$dOCLCO$dOCLCQ$dUMI$dDEBBG$dCSJ$dS3O$dYDX$dMERUC$dERL$dOCLCQ$dCEF$dOCLCQ$dTSC$dOCLCQ$dOCLCF$dOCLCO$dOCLCQ$dBRF$dOCLCO
019 $a846860023$a860582711$a880637669$a971039283$a971074534$a995765340$a1003145047
020 $a1439860920$q(electronic bk.)
020 $a9781439860922$q(electronic bk.)
020 $z9781439860915
020 $z1439860912
035 $a(OCoLC)774956267$z(OCoLC)846860023$z(OCoLC)860582711$z(OCoLC)880637669$z(OCoLC)971039283$z(OCoLC)971074534$z(OCoLC)995765340$z(OCoLC)1003145047
037 $aCL0500000427$bSafari Books Online
050 4 $aHF5415.126$b.R38 2012
072 7 $aBUS$x090010$2bisacsh
072 7 $aBUS$x040000$2bisacsh
072 7 $aBUS$x043010$2bisacsh
072 7 $aBUS$x043040$2bisacsh
072 7 $aBUS$x043050$2bisacsh
082 04 $a658.8/72$222
049 $aZCUA
100 1 $aRatner, Bruce,$eauthor.
245 10 $aStatistical and machine-learning data mining :$btechniques for better predictive modeling and analysis of big data /$cBruce Ratner.
250 $aSecond edition.
264 1 $aBoca Raton, FL :$bCRC Press,$c[2011]
264 4 $c©2011
300 $a1 online resource (xxv, 496 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
500 $aRevised edition of: Statistical modeling and analysis for database marketing. c2003.
504 $aIncludes bibliographical references.
520 $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.
505 0 $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?
505 8 $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.
505 8 $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.
505 8 $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.
650 0 $aDatabase marketing$xStatistical methods.
650 0 $aData mining$xStatistical methods.
650 0 $aBig data$xStatistical methods.
650 6 $aBases de données$xMarketing$xMéthodes statistiques.
650 6 $aDonnées volumineuses$xMéthodes statistiques.
650 7 $aBUSINESS & ECONOMICS$xE-Commerce$xInternet Marketing.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xMail Order.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xMarketing$xDirect.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xMarketing$xMultilevel.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xMarketing$xTelemarketing.$2bisacsh
650 7 $aData mining$xStatistical methods.$2fast$0(OCoLC)fst02007323
650 7 $aWissensextraktion.$2idszbz
650 7 $aStatistik.$2idszbz
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aRatner, Bruce.$tStatistical modeling and analysis for database marketing.
776 08 $iPrint version:$aRatner, Bruce.$tStatistical and machine-learning data mining.$b2nd ed.$dBoca Raton, FL : Taylor & Francis, ©2012$z9781439860915$w(DLC) 2011014298$w(OCoLC)663952432
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15083552$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS