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A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbor, learning vector quantization, random forest, support vector machine, … It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are processed the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots.

Installation

You can install the released version of {mlearning} from CRAN with:

install.packages("mlearning")

You can also install the latest development version. Make sure you have the {remotes} R package installed:

install.packages("remotes")

Use install_github() to install the {mlearning} package from GitHub (source from master branch will be recompiled on your machine):

remotes::install_github("SciViews/mlearning")

R should install all required dependencies automatically, and then it should compile and install {mlearning}.

Latest devel version of {mlearning} (source + Windows binaries for the latest stable version of R at the time of compilation) is also available from appveyor.

Further explore {mlearning}

You can get further help about this package this way: Make the {mlearning} package available in your R session:

Get help about this package:

library(help = "mlearning")
help("mlearning-package")

For further instructions, please, refer to the help pages at https://www.sciviews.org/mlearning/.

Code of Conduct

Please note that the {mlearning} package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.