The caret Package

Max Kuhn

2019-03-27

1 Introduction

The caret package (short for Classification And REgression Training) 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.

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