An 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.
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}.
You can get further help about this package this way: make the {mlearning} package available in your R session:
library("mlearning")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/.
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.