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