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)