The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting pre-processing feature selection model tuning using resampling variable importance estimation

as well as other functionality. There are many different modeling functions in R. Some have different syntax for model training and/or prediction. The package started off as a way to provide a uniform interface the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance).

The current release version can be found on CRAN and the project is hosted on github.

Some resources: The book Applied Predictive Modeling features caret and over 40 other R packages. It is on sale at Amazon or the the publisher’s website. There is a companion website too. There is also a paper on caret in the Journal of Statistical Software. The example data can be obtained here(the predictors) and here (the outcomes). There is a webinar for the package on Youtube that was organized and recorded by Ray DiGiacomo Jr for the Orange County R User Group. At useR! 2014, I was interviewed and discussed the package and the book. DataCamp has a beginner’s tutorial on machine learning in R using caret.

You can always email me with questions,comments or suggestions. These HTML pages were created using bookdown.