By Max Kuhn,Kjell Johnson
Applied Predictive Modeling covers the general predictive modeling strategy, starting with the the most important steps of knowledge preprocessing, information splitting and foundations of version tuning. The textual content then offers intuitive reasons of diverse universal and smooth regression and class options, consistently with an emphasis on illustrating and fixing actual information difficulties. Addressing sensible matters extends past version becoming to subject matters corresponding to dealing with category imbalance, making a choice on predictors, and pinpointing reasons of negative version performance―all of that are difficulties that ensue usually in practice.
The textual content illustrates all components of the modeling technique via many hands-on, real-life examples. And each bankruptcy includes broad R code for every step of the method. The facts units and corresponding code are available the book's significant other AppliedPredictiveModeling R package deal, that's freely to be had at the CRAN archive.
This multi-purpose textual content can be utilized as an creation to predictive types and the general modeling strategy, a practitioner's reference guide, or as a textual content for complex undergraduate or graduate point predictive modeling classes. To that finish, every one bankruptcy includes challenge units to aid solidify the lined suggestions and makes use of info on hand within the book's R package.
Readers and scholars attracted to imposing the equipment must have a few simple wisdom of R. And a handful of the extra complicated themes require a few mathematical knowledge.
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Additional info for Applied Predictive Modeling
Applied Predictive Modeling by Max Kuhn,Kjell Johnson