Package index • mlearning

All functions
`confusion()` `print(<confusion>)` `summary(<confusion>)` `print(<summary.confusion>)`	Construct and analyze confusion matrices
`mlKnn()` `ml_knn()` `summary(<mlKnn>)` `print(<summary.mlKnn>)` `predict(<mlKnn>)`	Supervised classification using k-nearest neighbor
`mlLda()` `ml_lda()` `predict(<mlLda>)`	Supervised classification using linear discriminant analysis
`mlLvq()` `ml_lvq()` `summary(<mlLvq>)` `print(<summary.mlLvq>)` `predict(<mlLvq>)`	Supervised classification using learning vector quantization
`mlNaiveBayes()` `ml_naive_bayes()` `predict(<mlNaiveBayes>)`	Supervised classification using naive Bayes
`mlNnet()` `ml_nnet()` `predict(<mlNnet>)`	Supervised classification and regression using neural network
`mlQda()` `ml_qda()` `predict(<mlQda>)`	Supervised classification using quadratic discriminant analysis
`mlRforest()` `ml_rforest()` `predict(<mlRforest>)`	Supervised classification and regression using random forest
`mlRpart()` `ml_rpart()` `predict(<mlRpart>)`	Supervised classification and regression using recursive partitioning
`mlSvm()` `ml_svm()` `predict(<mlSvm>)`	Supervised classification and regression using support vector machine
`mlearning-package`	'SciViews::R' - Machine Learning Algorithms with Unified Interface
`mlearning()` `print(<mlearning>)` `summary(<mlearning>)` `print(<summary.mlearning>)` `plot(<mlearning>)` `predict(<mlearning>)` `cvpredict()`	Machine learning model for (un)supervised classification or regression
`plot(<confusion>)` `confusion_image()` `confusionImage()` `confusion_barplot()` `confusionBarplot()` `confusion_stars()` `confusionStars()` `confusion_dendrogram()` `confusionDendrogram()`	Plot a confusion matrix
`prior()` `prior<-`()	Get or set priors on a confusion matrix
`response()`	Get the response variable for a mlearning object
`train()`	Get the training variable for a mlearning object

Reference

All functions