| 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 |