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:
mlearning_1.2.1.tar.gz
mlearning_1.2.1.zip(r-4.5)mlearning_1.2.1.zip(r-4.4)mlearning_1.2.1.zip(r-4.3)
mlearning_1.2.1.tgz(r-4.4-any)mlearning_1.2.1.tgz(r-4.3-any)
mlearning_1.2.1.tar.gz(r-4.5-noble)mlearning_1.2.1.tar.gz(r-4.4-noble)
mlearning_1.2.1.tgz(r-4.4-emscripten)mlearning_1.2.1.tgz(r-4.3-emscripten)
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')) |
Bug tracker:https://github.com/sciviews/mlearning/issues
Last updated 11 months agofrom:2ff68b8445
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 page | Topics |
|---|---|
| Machine Learning Algorithms with Unified Interface and Confusion Matrices | mlearning-package |
| Construct and analyze confusion matrices | confusion confusion.default confusion.mlearning print.confusion print.summary.confusion summary.confusion |
| Machine learning model for (un)supervised classification or regression | cvpredict cvpredict.mlearning mlearning plot.mlearning predict.mlearning print.mlearning print.summary.mlearning summary.mlearning |
| Supervised classification using k-nearest neighbor | mlKnn mlKnn.default mlKnn.formula ml_knn predict.mlKnn print.summary.mlKnn summary.mlKnn |
| Supervised classification using linear discriminant analysis | mlLda mlLda.default mlLda.formula ml_lda predict.mlLda |
| Supervised classification using learning vector quantization | mlLvq mlLvq.default mlLvq.formula ml_lvq predict.mlLvq print.summary.mlLvq summary.mlLvq |
| Supervised classification using naive Bayes | mlNaiveBayes mlNaiveBayes.default mlNaiveBayes.formula ml_naive_bayes predict.mlNaiveBayes |
| Supervised classification and regression using neural network | mlNnet mlNnet.default mlNnet.formula ml_nnet predict.mlNnet |
| Supervised classification using quadratic discriminant analysis | mlQda mlQda.default mlQda.formula ml_qda predict.mlQda |
| Supervised classification and regression using random forest | mlRforest mlRforest.default mlRforest.formula ml_rforest predict.mlRforest |
| Supervised classification and regression using recursive partitioning | mlRpart mlRpart.default mlRpart.formula ml_rpart predict.mlRpart |
| Supervised classification and regression using support vector machine | mlSvm mlSvm.default mlSvm.formula ml_svm predict.mlSvm |
| Plot a confusion matrix | confusionBarplot confusionDendrogram confusionImage confusionStars confusion_barplot confusion_dendrogram confusion_image confusion_stars plot.confusion |
| Get or set priors on a confusion matrix | prior prior.confusion prior<- prior<-.confusion |
| Get the response variable for a mlearning object | response response.default |
| Get the training variable for a mlearning object | train train.default |
