The major significance and special interest is in Chapter 15, where I talk about statistics and machine learning, and I define that statistical learning is the ability to solve statistical problems using non-statistical machine learning. GenIQ is now presented as such a non-statistical machine learning model. Moreover, in Chapter 16 GenIQ serves as an effective method for finding the best possible subset of variables for a model.- from the original and newly genetically data mined variables. The later suggests that GenIQ is a data mining tool par excellence. 

Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models.By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.

A consultant to the database marketing industry describes specific methods for solving the most commonly experienced problems encountered in the field. He also provides a background by discussing the basic methodologies of database analysis and modeling. Among the problems he addresses are variable assessment, response modeling, profit modeling, and market segment classification modeling.

As the Preface (p. iii) begins, “This book is a compilation of essays.” That is the only negative comment that I have about this book, which is remarkable if one considers the tone of the comments that I have made in the various reports that I have served up in these pages for a host of unexceptional data mining books. For one thing, this book is not at all focused on the myriad of data processing steps that data mining practitioners undertake once their datasets are available. For another thing, the essays in this book were written by a statistician. Subtitled “Effective Techniques for Mining Big Data,” the book focuses strictly on modeling with a designated dependent variable. Also, the author builds a historical basis for data mining that includes EDA as a significant catalyst — a perspective that should interest most statisticians

An essential book for statistical analysts building predictive models for database marketing. This is a must have introductory book for the practitioner using data mining to build predictive models in industry. While it does have a few snippets of SAS code, it is a conceptual book that explains the "why" and the "how" of practical model building. It dispenses with  the antiquated notion of the "true" model of classical statistics and econometrics, and shows how to arrive at an acceptable model that yeilds good predictions. As practitioner's, this is what we care about most. Among other things, it gives good explanations of: (1) the EDA paradigm versus classical statistics (2) Tukey's bulging rule for transforming variables (3) variable selection. It discusses some of the weaknesses of automatic variable selection methods (4) smoothed scatterplots and logit plots (5) decile analysis and using bootstrapping to derive confidence intervals for cum lift.

The book shows you how to use logistic regression, OLS, and CHAID to build predictive models. For those interested in Genetic modeling, it has a clearly written chapter on the subject that explains how genetic modeling can be used to create new variables that can have more information than either of the original variables. 

I consider myself fortunate to be the first to review this book. The title aptly indicates what the book is about: Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data. The author provides in a Tukey-esque manner a collection of solutions to common problems facing database analysts, model builders, and marketers. The book can uniquely serve as a textbook, a how-to guide, and a reference source depending on the reader's statistical training and database marketing experience. Moreover, the author actually goes where other authors provide lip service: he creates the marriage of the "old" statistical methodologies with the new machine learning influence by introducing machine learning methods specifically tailored to database assessment of optimal model performance. The book's illustrations involve real problems, real data, and better solutions. This book is a keeper!

I work for an insurance company in a small department of statisticians (5 people), and we usually bring in text books, articles, presentation copies  for periodical discussions. I brought in a book just purchased to discuss with my colleagues -your excellent book - Statistical Modeling and Analysis for Database Marketing. 

Dr. Ratner should be commended for writing this book that is truly original. As far as I am concerned there is no other book of its kind: most books present old  topics and new subject matter in various forms without real change or improvement in substance. Each of Ratner's chapters is unique: a common problem is presented and a novel, superior solution (over the typical solution approach) is discussed. And, new subject matter is presented with an orignal and viable solution. Dr. Ratner shares his knowledge and has a lot of it to go around.

Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data   provides an excellent treatment of statistical modeling for database marketers. The book explains clearly how statistical modeling should be done in practice. In particular, I can see where the GenIQ Model can come handy in subselection of predictors and smoothing the variables. The book is a must for every statistical database marketing analyst and model builder.