By Max Kuhn,Kjell Johnson
Applied Predictive Modeling covers the final predictive modeling procedure, starting with the an important steps of knowledge preprocessing, information splitting and foundations of version tuning. The textual content then presents intuitive reasons of diverse universal and smooth regression and class innovations, continually with an emphasis on illustrating and fixing actual information difficulties. Addressing useful matters extends past version becoming to subject matters similar to dealing with classification imbalance, picking predictors, and pinpointing factors of negative version performance―all of that are difficulties that happen often in practice.
The textual content illustrates all components of the modeling method via many hands-on, real-life examples. And each bankruptcy comprises vast R code for every step of the method. The information units and corresponding code are available the book's spouse 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 final modeling technique, a practitioner's reference instruction manual, or as a textual content for complicated undergraduate or graduate point predictive modeling classes. To that finish, every one bankruptcy includes challenge units to aid solidify the coated options and makes use of info to be had within the book's R package.
Readers and scholars attracted to enforcing the tools 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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Extra resources for Applied Predictive Modeling
Applied Predictive Modeling by Max Kuhn,Kjell Johnson