Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data (Record no. 52078)

MARC details
000 -LEADER
fixed length control field 03461nam a22002297a 4500
003 - CONTROL NUMBER IDENTIFIER
control field ISURa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191126b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781498797603
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English Language
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number RAT
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ratner, Bruce
9 (RLIN) 74274
245 ## - TITLE STATEMENT
Title Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data
250 ## - EDITION STATEMENT
Edition statement 3rd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc New York
Name of publisher, distributor, etc Boca Raton, FL : CRC Press,
Date of publication, distribution, etc 2011
300 ## - PHYSICAL DESCRIPTION
Extent xxxiii, 653 p.
Dimensions 26 cm.
500 ## - GENERAL NOTE
General note 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.
520 ## - SUMMARY, ETC.
Summary, etc <br/>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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
9 (RLIN) 75286
Topical term or geographic name as entry element Machine learning
9 (RLIN) 69133
Topical term or geographic name as entry element Special computer method
9 (RLIN) 75287
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Lending Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Date checked out Price effective from Koha item type
    Dewey Decimal Classification     Lending Collection Applied Sciences Library Applied Sciences Library Lending Section 26/11/2019 16837.00 1 006.31 RAT 113560 29/03/2021 19/03/2021 26/11/2019 Lending Books
    Dewey Decimal Classification     Reference Collection Applied Sciences Library Applied Sciences Library Reference Section 26/11/2019 16837.00   006.31 RAT 113559 26/11/2019   26/11/2019 Sheduled Reference