Conditional Inference Random Forest

method = 'cforest'

Type: Classification, Regression

Tuning Parameters: mtry (#Randomly Selected Predictors)

Oblique Random Forest

method = 'ORFlog'

Type: Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Oblique Random Forest

method = 'ORFpls'

Type: Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Oblique Random Forest

method = 'ORFridge'

Type: Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Oblique Random Forest

method = 'ORFsvm'

Type: Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Parallel Random Forest

method = 'parRF'

Type: Classification, Regression

Tuning Parameters: mtry (#Randomly Selected Predictors)

Quantile Random Forest

method = 'qrf'

Type: Regression

Tuning Parameters: mtry (#Randomly Selected Predictors)

Random Ferns

method = 'rFerns'

Type: Classification

Tuning Parameters: depth (Fern Depth)

Random Forest

method = 'rf'

Type: Classification, Regression

Tuning Parameters: mtry (#Randomly Selected Predictors)

Random Forest by Randomization

method = 'extraTrees'

Type: Regression, Classification

Tuning Parameters: mtry (# Randomly Selected Predictors), numRandomCuts (# Random Cuts)

Random Forest with Additional Feature Selection

method = 'Boruta'

Type: Regression, Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Regularized Random Forest

method = 'RRF'

Type: Regression, Classification

Tuning Parameters: mtry (#Randomly Selected Predictors), coefReg (Regularization Value), coefImp (Importance Coefficient)

Regularized Random Forest

method = 'RRFglobal'

Type: Regression, Classification

Tuning Parameters: mtry (#Randomly Selected Predictors), coefReg (Regularization Value)

Stacked AutoEncoder Deep Neural Network

method = 'dnn'

Type: Classification, Regression

Tuning Parameters: layer1 (Hidden Layer 1), layer2 (Hidden Layer 2), layer3 (Hidden Layer 3), hidden_dropout (Hidden Dropouts), visible_dropout (Visible Dropout)