Bagged CART
method = 'treebag'
Type: Regression, Classification
No Tuning Parameters
Bagged Flexible Discriminant Analysis
method = 'bagFDA'
Type: Classification
Tuning Parameters: degree (Product Degree), nprune (#Terms)
Bagged Logic Regression
method = 'logicBag'
Type: Regression, Classification
Tuning Parameters: nleaves (Maximum Number of Leaves), ntrees (Number of Trees)
Bagged MARS
method = 'bagEarth'
Type: Regression, Classification
Tuning Parameters: nprune (#Terms), degree (Product Degree)
Bagged Model
method = 'bag'
Type: Regression, Classification
Tuning Parameters: vars (#Randomly Selected Predictors)
Conditional Inference Random Forest
method = 'cforest'
Type: Classification, Regression
Tuning Parameters: mtry (#Randomly Selected Predictors)
Model Averaged Neural Network
method = 'avNNet'
Type: Classification, Regression
Tuning Parameters: size (#Hidden Units), decay (Weight Decay), bag (Bagging)
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)
Quantile Regression Neural Network
method = 'qrnn'
Type: Regression
Tuning Parameters: n.hidden (#Hidden Units), penalty ( Weight Decay), bag (Bagged Models?)
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)
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)