Package: mlearning 1.2.1

Philippe Grosjean

mlearning: Machine Learning Algorithms with Unified Interface and Confusion Matrices

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.

Authors:Philippe Grosjean [aut, cre], Kevin Denis [aut]

mlearning_1.2.1.tar.gz
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mlearning.pdf |mlearning.html
mlearning/json (API)
NEWS

# Install mlearning in R:
install.packages('mlearning', repos = c('https://sciviews.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sciviews/mlearning/issues

On CRAN:

machine-learning

33 exports 1.24 score 29 dependencies 2 dependents

Last updated 10 months agofrom:2ff68b8445b5538b2e4362b2f505c68ced6caa1e

Exports:confusionconfusion_barplotconfusion_dendrogramconfusion_imageconfusion_starsconfusionBarplotconfusionDendrogramconfusionImageconfusionStarscvpredictml_knnml_ldaml_lvqml_naive_bayesml_nnetml_qdaml_rforestml_rpartml_svmmlearningmlKnnmlLdamlLvqmlNaiveBayesmlNnetmlQdamlRforestmlRpartmlSvmpriorprior<-responsetrain

Dependencies:classclicodetoolsdata.tablediagramdigeste1071futurefuture.applyglobalsipredKernSmoothlatticelavalistenvMASSMatrixnnetnumDerivparallellyprodlimprogressrproxyrandomForestRcpprpartshapeSQUAREMsurvival

Readme and manuals

Help Manual

Help pageTopics
Machine Learning Algorithms with Unified Interface and Confusion Matricesmlearning-package
Construct and analyze confusion matricesconfusion confusion.default confusion.mlearning print.confusion print.summary.confusion summary.confusion
Machine learning model for (un)supervised classification or regressioncvpredict cvpredict.mlearning mlearning plot.mlearning predict.mlearning print.mlearning print.summary.mlearning summary.mlearning
Supervised classification using k-nearest neighbormlKnn mlKnn.default mlKnn.formula ml_knn predict.mlKnn print.summary.mlKnn summary.mlKnn
Supervised classification using linear discriminant analysismlLda mlLda.default mlLda.formula ml_lda predict.mlLda
Supervised classification using learning vector quantizationmlLvq mlLvq.default mlLvq.formula ml_lvq predict.mlLvq print.summary.mlLvq summary.mlLvq
Supervised classification using naive BayesmlNaiveBayes mlNaiveBayes.default mlNaiveBayes.formula ml_naive_bayes predict.mlNaiveBayes
Supervised classification and regression using neural networkmlNnet mlNnet.default mlNnet.formula ml_nnet predict.mlNnet
Supervised classification using quadratic discriminant analysismlQda mlQda.default mlQda.formula ml_qda predict.mlQda
Supervised classification and regression using random forestmlRforest mlRforest.default mlRforest.formula ml_rforest predict.mlRforest
Supervised classification and regression using recursive partitioningmlRpart mlRpart.default mlRpart.formula ml_rpart predict.mlRpart
Supervised classification and regression using support vector machinemlSvm mlSvm.default mlSvm.formula ml_svm predict.mlSvm
Plot a confusion matrixconfusionBarplot confusionDendrogram confusionImage confusionStars confusion_barplot confusion_dendrogram confusion_image confusion_stars plot.confusion
Get or set priors on a confusion matrixprior prior.confusion prior<- prior<-.confusion
Get the response variable for a mlearning objectresponse response.default
Get the training variable for a mlearning objecttrain train.default