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)
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)
Stochastic Gradient Boosting
method = 'gbm'
Type: Regression, Classification
Tuning Parameters: n.trees (# Boosting Iterations), interaction.depth (Max Tree Depth), shrinkage (Shrinkage)