Statistical and Machine-Learning Data Mining
Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
2nd Edition
Published on 19. December 2011
542 pages
978-1-4665-5121-3 (ISBN)
Description
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: Eff
More details
Edition
2nd edition
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Quantitatively trained (PhD level or requirement-met BS, MS) statisticians, data miners, machine-learning data analysts, direct and database marketers, knowledge discoverers, CRM; analysts of customer and corporate risk, competitive and customer intelligence, consumer and corporate credit card, financial service marketing, customer lifecycle management, strategic business and marketing intelligence, and research methods.
Edition type
Revised edition
Illustrations
183 s/w Abbildungen, 155 s/w Tabellen
183 b/w images, 155 tables and at least 75 equations - Last print over 600 copies at IBT - Also at PGK
ISBN-13
978-1-4665-5121-3 (9781466551213)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Bruce Ratner
Statistical and Machine-Learning Data Mining
Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
Book
12/2011
2nd Edition
CRC Press
€90.36
Article exhausted; check different version
Persons
Bruce Ratner, DM STAT-l Consulting
Content
Two Basic Data Mining Methods for Variable Assessment. CHAID-based Data Mining for Paired-Variable Assessment. The Importance of Straight Data. Symmetrizing Ranked Data. Principal Component Analysis. The Correlation Coefficient. Logistic Regression. Ordinary Regression. Variable Selection Methods in Regression. CHAID for Interpreting a Logistic Regression Model. The Importance of the Regression Coefficient.The Average Correlation. CHAID for Specifying a Model with Interaction Variables. Market-segmentation Classification Modeling with Logistic Regression. CHAID as a Method for Filling in Missing Values. Identifying Your Best Customers. Assessment of Marketing Models. Bootstrapping in Marketing. Validating the Logistic Regression Model. Visualization of Marketing Models. The Predictive Contribution Coefficient. Regression Modeling Involves Art, Science, and Poetry Too. Genetic and Statistic Regression Models. A Data Mining Method for Moderating Outliers, Instead of Discarding Them. Overfitting. The Importance of Straight Data. The GenIQ Model. Finding the Best Variables for Marketing Models. Interpretation of Coefficient-Free Models.
