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)
Boosted Classification Trees
method = 'ada'
Type: Classification
Tuning Parameters: iter (#Trees), maxdepth (Max Tree Depth), nu (Learning Rate)
Boosted Generalized Additive Model
method = 'gamboost'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), prune (AIC Prune?)
Boosted Generalized Linear Model
method = 'glmboost'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), prune (AIC Prune?)
Boosted Linear Model
method = 'bstLs'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), nu (Shrinkage)
Boosted Logistic Regression
method = 'LogitBoost'
Type: Classification
Tuning Parameters: nIter (# Boosting Iterations)
Boosted Smoothing Spline
method = 'bstSm'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), nu (Shrinkage)
Boosted Tree
method = 'blackboost'
Type: Regression, Classification
Tuning Parameters: mstop (#Trees), maxdepth (Max Tree Depth)
Boosted Tree
method = 'bstTree'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), maxdepth (Max Tree Depth), nu (Shrinkage)
C5.0
method = 'C5.0'
Type: Classification
Tuning Parameters: trials (# Boosting Iterations), model (Model Type), winnow (Winnow)
Conditional Inference Random Forest
method = 'cforest'
Type: Classification, Regression
Tuning Parameters: mtry (#Randomly Selected Predictors)
Cost-Sensitive C5.0
method = 'C5.0Cost'
Type: Classification
Tuning Parameters: trials (# Boosting Iterations), model (Model Type), winnow (Winnow), cost (Cost)
Cubist
method = 'cubist'
Type: Regression
Tuning Parameters: committees (#Committees), neighbors (#Instances)
Model Averaged Neural Network
method = 'avNNet'
Type: Classification, Regression
Tuning Parameters: size (#Hidden Units), decay (Weight Decay), bag (Bagging)
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)
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)
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)
Stochastic Gradient Boosting
method = 'gbm'
Type: Regression, Classification
Tuning Parameters: n.trees (# Boosting Iterations), interaction.depth (Max Tree Depth), shrinkage (Shrinkage)
Tree-Based Ensembles
method = 'nodeHarvest'
Type: Regression, Classification
Tuning Parameters: maxinter (Maximum Interaction Depth), mode (Prediction Mode)