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 |