Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition by Bruce Ratner (Author). The second version of a bestseller, Statistical and Machine-Learning Data Mining: Strategies for Better Predictive Modeling and Evaluation of Large Knowledge is still the only e book, so far, to tell apart between statistical knowledge mining and machine-studying information mining. The primary edition, titled Statistical Modeling and Analysis for Database Advertising and marketing: Efficient Methods for Mining Large Data, contained 17 chapters of progressive and practical statistical data mining techniques. In this second version, renamed to replicate the increased protection of machine-learning knowledge mining strategies, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of artistic and helpful machine-studying data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative strategies make this ebook distinctive within the field of data mining literature.
The statistical information mining strategies effectively consider big knowledge for figuring out structures (variables) with the appropriate predictive power with a view to yield dependable and strong large-scale statistical fashions and analyses. In contrast, the creator's personal GenIQ Mannequin supplies machine-studying options to frequent and nearly unapproachable statistical problems. GenIQ makes this possible - its utilitarian knowledge mining features begin where statistical data mining stops.
This ebook accommodates essays offering detailed background, dialogue, and illustration of specific methods for solving the most commonly experienced issues in predictive modeling and analysis of huge data. They tackle each methodology and assign its application to a selected kind of problem. To raised floor readers, the ebook provides an in-depth dialogue of the essential methodologies of predictive modeling and analysis. Whereas this kind of overview has been attempted earlier than, this approach gives a really nitty-gritty, step-by-step method that each tyros and experts in the discipline can take pleasure in playing with.
I really enjoyed studying the ebook - Statistical and Machine-Learning Knowledge Mining which was written by Dr. Ratner. My favorite part of this ebook is that it offers the detailed process to assist the learner to know learn how to create an information mining structure and predictive fashions using totally different techniques. My firm has invited Dr. Ratner to be a statistical marketing consultant to assist us to develop a predictive mannequin by using the GenIQ approach. The new mannequin did a terrific job to succeed in a better accuracy rate in comparison with the outdated model. I strongly advocate this e-book to anybody within the discipline of knowledge mining searching for a sensible application.
Dr Ratner's overview of those subjects hits some sweet spots with me. I'm all the time trying to perceive trends in analytic instruments and techniques that may provide my firm with a aggressive advantage. The probem I find in up and coming tools/techniques, is that you must be the esoteric statistician or mathematacian in the specific discipline to know the capabilities, not to mention, the right way to use them. This e-book is much more positively utlitarian in design. It offers loads of examples and permits someone with a basic understanding of scientific or engineering statistics access to machine studying and knowledge mining techniques. It is a great text for: 1)these in business or NGO's exploring "Massive Information" evaluation; 2) supervisor's determining buget allocations for tools (which I believe this e book shows you don't necessarily need enterprise sofware packages); or 3) a survey course on these two topics. And some added bonuses: the author writes in a straightforward to learn model and is greater than prepared to answer questions or have discussions on the topics.
Statistical and Machine-Learning Data Mining: Techniques for Better
Predictive Modeling and Analysis of Big Data, Second Edition
Bruce
Ratner (Author)
542 pages
CRC Press; 2 edition (December 19, 2011)
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