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)