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mlearning  

Machine Learning Algorithms with Unified Interface and Confusion Matrices
View on CRAN: Click here


Download and install mlearning package within the R console
Install from CRAN:
install.packages("mlearning")

Install from Github:
library("remotes")
install_github("cran/mlearning")

Install by package version:
library("remotes")
install_version("mlearning", "1.2.1")



Attach the package and use:
library("mlearning")
Maintained by
Philippe Grosjean
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-01-30
Latest Update: 2023-08-30
Description:
A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, 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.
How to cite:
Philippe Grosjean (2013). mlearning: Machine Learning Algorithms with Unified Interface and Confusion Matrices. R package version 1.2.1, https://cran.r-project.org/web/packages/mlearning
Previous versions and publish date:
1.0-0 (2013-01-30 22:31), 1.1.1 (2022-04-26 13:40)
Other packages that cited mlearning R package
View mlearning citation profile
Other R packages that mlearning depends, imports, suggests or enhances
Functions, R codes and Examples using the mlearning R package
Some associated functions: confusion . mlKnn . mlLda . mlLvq . mlNaiveBayes . mlNnet . mlQda . mlRforest . mlRpart . mlSvm . mlearning-package . mlearning . plot.confusion . prior . response . train . 
Some associated R codes: confusion.R . ml_knn.R . ml_lda.R . ml_lvq.R . ml_naive_bayes.R . ml_nnet.R . ml_qda.R . ml_rforest.R . ml_rpart.R . ml_svm.R . mlearning-package.R . mlearning.R . plot.confusion.R . prior.R . response.R . train.R .  Full mlearning package functions and examples
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