Bagged CART

method = 'treebag'

Type: Regression, Classification

No Tuning Parameters

Boosted Classification Trees

method = 'ada'

Type: Classification

Tuning Parameters: iter (#Trees), maxdepth (Max Tree Depth), nu (Learning Rate)

Boosted Logistic Regression

method = 'LogitBoost'

Type: Classification

Tuning Parameters: nIter (# Boosting Iterations)

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)

C4.5-like Trees

method = 'J48'

Type: Classification

Tuning Parameters: C (Confidence Threshold)

C5.0

method = 'C5.0'

Type: Classification

Tuning Parameters: trials (# Boosting Iterations), model (Model Type), winnow (Winnow)

CART

method = 'rpart'

Type: Regression, Classification

Tuning Parameters: cp (Complexity Parameter)

CART

method = 'rpart2'

Type: Regression, Classification

Tuning Parameters: maxdepth (Max Tree Depth)

Conditional Inference Tree

method = 'ctree'

Type: Classification, Regression

Tuning Parameters: mincriterion (1 - P-Value Threshold)

Conditional Inference Tree

method = 'ctree2'

Type: Regression, Classification

Tuning Parameters: maxdepth (Max Tree Depth)

Cost-Sensitive C5.0

method = 'C5.0Cost'

Type: Classification

Tuning Parameters: trials (# Boosting Iterations), model (Model Type), winnow (Winnow), cost (Cost)

Cost-Sensitive CART

method = 'rpartCost'

Type: Classification

Tuning Parameters: cp (Complexity Parameter), Cost (Cost)

Model Tree

method = 'M5'

Type: Regression

Tuning Parameters: pruned (Pruned), smoothed (Smoothed), rules (Rules)

Oblique Trees

method = 'oblique.tree'

Type: Classification

Tuning Parameters: oblique.splits (Oblique Splits), variable.selection (Variable Selection Method)

Random Forest with Additional Feature Selection

method = 'Boruta'

Type: Regression, Classification

Tuning Parameters: mtry (#Randomly Selected Predictors)

Single C5.0 Tree

method = 'C5.0Tree'

Type: Classification

No Tuning Parameters

Stochastic Gradient Boosting

method = 'gbm'

Type: Regression, Classification

Tuning Parameters: n.trees (# Boosting Iterations), interaction.depth (Max Tree Depth), shrinkage (Shrinkage)

Tree Models from Genetic Algorithms

method = 'evtree'

Type: Regression, Classification

Tuning Parameters: alpha (Complexity Parameter)

Tree-Based Ensembles

method = 'nodeHarvest'

Type: Regression, Classification

Tuning Parameters: maxinter (Maximum Interaction Depth), mode (Prediction Mode)