R: Machine Learning Algorithms with Unified Interface and Confusion Matrices

# Machine Learning Algorithms with Unified Interface and Confusion Matrices

## Documentation for package ‘mlearning’ version 1.2.1

- DESCRIPTION file
- Package NEWS

## Help Pages

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