STATISTICAL AND MACHINE-LEARNING DATA MINING BRUCE RATNER PDF

About this title 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 completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

Author:Aracage Arar
Country:Russian Federation
Language:English (Spanish)
Genre:Music
Published (Last):23 May 2019
Pages:26
PDF File Size:15.96 Mb
ePub File Size:15.12 Mb
ISBN:728-6-91454-446-6
Downloads:3016
Price:Free* [*Free Regsitration Required]
Uploader:Zulkijora



Predicting Share of Wallet without Survey Data The Importance of the Regression Coefficient Art, Science, Numbers, and Poetry Assessment of Marketing Models Decile Analysis: Perspective and Performance Overfitting: Old Problem, New Solution The Importance of Straight Data: Revisited Finding the Best Variables for Marketing Models Interpretation of Coefficient-Free Models Some of My Favorite Statistical Subroutines Index show more Review quote "I bought your book as it seemed to have the right mixture of statistical theory, practice, and common sense - finally!

You can find the first often; the second occasionally; but the third, esp. I cannot thank you enough, Bruce! You are brilliant at assimilating, stating the underlying principles of analyses. It provides insightful methods for data mining, and innovative techniques for predictive analytics. The book is a valuable resource for experienced and newbie data scientists. It is written in a clear style, and is an enjoyable read as it includes historical notes, which flow with the material.

Theurer Assoc. It offers many insightful perspectives to use for future ALM features and improvements. This book is an excellent contribution to the literature of statistics, data mining, and machine learning.

Thank you, Bruce. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods.

He holds a patent for a unique application in solving the two-group classification problem with genetic programming. Learn about new offers and get more deals by joining our newsletter Sign up now.

ELAN Z660 INSTALLATIONS PDF

ISBN 13: 9781439860915

Predicting Share of Wallet without Survey Data The Importance of the Regression Coefficient Art, Science, Numbers, and Poetry Assessment of Marketing Models Decile Analysis: Perspective and Performance

EN 50379-3 PDF

Statistical 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 completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures variables with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses.

Related Articles