By Bruce Ratner
The moment version of a bestseller, Statistical and Machine-Learning facts Mining: strategies for larger Predictive Modeling and research of huge Data continues to be the single booklet, thus far, to tell apart among statistical information mining and machine-learning info mining. the 1st variation, titled Statistical Modeling and research for Database advertising and marketing: powerful ideas for Mining mammoth Data, contained 17 chapters of leading edge and functional statistical facts mining thoughts. during this moment version, renamed to mirror the elevated assurance of machine-learning information mining options, the writer has thoroughly revised, reorganized, and repositioned the unique chapters and produced 14 new chapters of artistic and necessary machine-learning facts mining options. In sum, the 31 chapters of straightforward but insightful quantitative ideas make this ebook distinct within the box of information mining literature.
The statistical facts mining equipment successfully ponder immense info for deciding on buildings (variables) with the precise predictive energy to be able to yield trustworthy and powerful large-scale statistical versions and analyses. by contrast, the author's personal GenIQ version presents machine-learning suggestions to universal and nearly unapproachable statistical difficulties. GenIQ makes this attainable ― its utilitarian info mining good points commence the place statistical facts mining stops.
This ebook includes essays supplying specified heritage, dialogue, and representation of particular tools for fixing the main more often than not skilled difficulties in predictive modeling and research of huge facts. They tackle every one technique and assign its program to a particular form of challenge. to raised flooring readers, the ebook presents an in-depth dialogue of the fundamental methodologies of predictive modeling and research. whereas this kind of evaluation has been tried earlier than, this procedure deals a very nitty-gritty, step by step approach that either tyros and specialists within the box can get pleasure from enjoying with.
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Extra resources for Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
Example text
Thus, calculating the average income from a database of 2 million individuals requires heavy-duty lifting (number crunching). In terms of learning or uncovering the structure among the variables, big can be considered 50 variables or more. Regardless of which side the data analyst is working, EDA scales up for both rows and columns of the data table. 1 Data Size Characteristics There are three distinguishable characteristics of data size: condition, location, and population. Condition refers to the state of readiness of the data for analysis.
The new method has the potential of exposing a more reliable depiction of the unmasked relationship for paired-variable assessment than that of the smoothed scatterplot. In Chapter 4, I show the importance of straight data for the simplicity and desirability it brings for good model building. In Chapter 5, I introduce the method of symmetrizing ranked data and add it to the paradigm of simplicity and desirability presented in Chapter 4. Principal component analysis, the popular data reduction technique invented in 1901, is repositioned in Chapter 6 as a data mining method for many-variable assessment.
Creative use of well-known techniques is further carried out in Chapter 15, where I solve the problem of market segment classification modeling using not only logistic regression but also CHAID. In Chapter 16, CHAID is yet again utilized in a somewhat unconventional manner—as a method for filling in missing values in one’s data. To bring an interesting real-life problem into the picture, I wrote Chapter 17 to describe profiling techniques for the marketer who wants a method for identifying his or her best customers.
