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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. Pdf checkpoint 1 9 7 equals.
Some resources:
- 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. Pdf checkpoint 1 9 7 equals.
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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