Function reference • 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: Machine Learning Algorithms with Unified Interface and Confusion Matrices

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