Statistical and Machine-Learning Data Mining: Techniques for by Bruce Ratner

By Bruce Ratner

The moment variation of a bestseller, Statistical and Machine-Learning information Mining: concepts for greater Predictive Modeling and research of huge Data remains to be the single booklet, to this point, to tell apart among statistical info mining and machine-learning information mining. the 1st version, titled Statistical Modeling and research for Database advertising: potent ideas for Mining enormous Data, contained 17 chapters of leading edge and sensible statistical info mining recommendations. during this moment version, renamed to mirror the elevated assurance of machine-learning facts mining concepts, the writer has thoroughly revised, reorganized, and repositioned the unique chapters and produced 14 new chapters of inventive and important machine-learning information mining recommendations. In sum, the 31 chapters of easy but insightful quantitative options make this ebook specified within the box of information mining literature.

The statistical info mining equipment successfully think of great facts for choosing buildings (variables) with the suitable predictive strength which will yield trustworthy and strong large-scale statistical types and analyses. against this, the author's personal GenIQ version presents machine-learning strategies to universal and almost unapproachable statistical difficulties. GenIQ makes this attainable ― its utilitarian facts mining gains begin the place statistical information mining stops.

This e-book includes essays supplying particular historical past, dialogue, and representation of particular equipment for fixing the main typically skilled difficulties in predictive modeling and research of massive info. They handle each one method and assign its software to a selected form of challenge. to higher flooring readers, the ebook offers an in-depth dialogue of the elemental methodologies of predictive modeling and research. whereas this sort of evaluation has been tried earlier than, this process deals a very nitty-gritty, step by step strategy that either tyros and specialists within the box can take pleasure in taking part in with.

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Chapter 21 offers a pair of graphics or visual displays that have value beyond the commonly used exploratory phase of analysis. In this chapter, I demonstrate the hitherto untapped potential for visual displays to describe the functionality of the final model once it has been implemented for prediction. I close the statistical data mining part of the book with Chapter 22, in which I offer a data-mining alternative measure, the predictive contribution coefficient, to the standardized coefficient.

4 The EDA Paradigm EDA presents a major paradigm shift in the ways models are built. With the mantra, “Let your data be your guide,” EDA offers a view that is a complete reversal of the classical principles that govern the usual steps of model building. EDA declares the model must always follow the data, not the other way around, as in the classical approach. In the classical approach, the problem is stated and formulated in terms of an outcome variable Y. It is assumed that the true model explaining all the variation in Y is known.

Extending the concept of coefficient, I introduce the average correlation coefficient in Chapter 13 to provide a quantitative criterion for assessing competing predictive models and the importance of the predictor variables. In Chapter 14, I demonstrate how to increase the predictive power of a model beyond that provided by its variable components. This is accomplished by creating an interaction variable, which is the product of two or more component variables. To test the significance of the interaction variable, I make what I feel to be a compelling case for a rather unconventional use of CHAID.

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