Bagged Logic Regression
method = 'logicBag'
Type: Regression, Classification
Tuning Parameters: nleaves (Maximum Number of Leaves), ntrees (Number of Trees)
Bayesian Generalized Linear Model
method = 'bayesglm'
Type: Regression, Classification
No Tuning Parameters
Boosted Generalized Linear Model
method = 'glmboost'
Type: Regression, Classification
Tuning Parameters: mstop (# Boosting Iterations), prune (AIC Prune?)
Factor-Based Linear Discriminant Analysis
method = 'RFlda'
Type: Classification
Tuning Parameters: q (# Factors)
Gaussian Process
method = 'gaussprLinear'
Type: Regression, Classification
No Tuning Parameters
Generalized Linear Model
method = 'glm'
Type: Regression, Classification
No Tuning Parameters
Generalized Linear Model with Stepwise Feature Selection
method = 'glmStepAIC'
Type: Regression, Classification
No Tuning Parameters
Generalized Partial Least Squares
method = 'gpls'
Type: Classification
Tuning Parameters: K.prov (#Components)
glmnet
method = 'glmnet'
Type: Regression, Classification
Tuning Parameters: alpha (Mixing Percentage), lambda (Regularization Parameter)
Heteroscedastic Discriminant Analysis
method = 'hda'
Type: Classification
Tuning Parameters: gamma (Gamma), lambda (Lambda), newdim (Dimension of the Discriminative Subspace)
High Dimensional Discriminant Analysis
method = 'hdda'
Type: Classification
Tuning Parameters: threshold (Threshold), model (Model Type)
Least Squares Support Vector Machine
method = 'lssvmLinear'
Type: Classification
No Tuning Parameters
Linear Discriminant Analysis
method = 'lda'
Type: Classification
No Tuning Parameters
Linear Discriminant Analysis
method = 'lda2'
Type: Classification
Tuning Parameters: dimen (#Discriminant Functions)
Linear Discriminant Analysis with Stepwise Feature Selection
method = 'stepLDA'
Type: Classification
Tuning Parameters: maxvar (Maximum #Variables), direction (Search Direction)
Logic Regression
method = 'logreg'
Type: Regression, Classification
Tuning Parameters: treesize (Maximum Number of Leaves), ntrees (Number of Trees)
Logistic Model Trees
method = 'LMT'
Type: Classification
Tuning Parameters: iter (# Iteratons)
Maximum Uncertainty Linear Discriminant Analysis
method = 'Mlda'
Type: Classification
No Tuning Parameters
Nearest Shrunken Centroids
method = 'pam'
Type: Classification
Tuning Parameters: threshold (Shrinkage Threshold)
Partial Least Squares
method = 'kernelpls'
Type: Regression, Classification
Tuning Parameters: ncomp (#Components)
Partial Least Squares
method = 'pls'
Type: Regression, Classification
Tuning Parameters: ncomp (#Components)
Partial Least Squares
method = 'simpls'
Type: Regression, Classification
Tuning Parameters: ncomp (#Components)
Partial Least Squares
method = 'widekernelpls'
Type: Regression, Classification
Tuning Parameters: ncomp (#Components)
Penalized Linear Discriminant Analysis
method = 'PenalizedLDA'
Type: Classification
Tuning Parameters: lambda (L1 Penalty), K (#Discriminant Functions)
Penalized Logistic Regression
method = 'plr'
Type: Classification
Tuning Parameters: lambda (L2 Penalty), cp (Complexity Parameter)
Penalized Multinomial Regression
method = 'multinom'
Type: Classification
Tuning Parameters: decay (Weight Decay)
Regularized Discriminant Analysis
method = 'rda'
Type: Classification
Tuning Parameters: gamma (Gamma), lambda (Lambda)
Robust Linear Discriminant Analysis
method = 'Linda'
Type: Classification
No Tuning Parameters
Robust Regularized Linear Discriminant Analysis
method = 'rrlda'
Type: Classification
Tuning Parameters: lambda (Penalty Parameter), hp (Robustness Parameter), penalty (Penalty Type)
Robust SIMCA
method = 'RSimca'
Type: Classification
No Tuning Parameters
Shrinkage Discriminant Analysis
method = 'sda'
Type: Classification
Tuning Parameters: diagonal (Diagonalize), lambda (shrinkage)
Sparse Linear Discriminant Analysis
method = 'sparseLDA'
Type: Classification
Tuning Parameters: NumVars (# Predictors), lambda (Lambda)
Sparse Partial Least Squares
method = 'spls'
Type: Regression, Classification
Tuning Parameters: K (#Components), eta (Threshold), kappa (Kappa)
Stabilized Linear Discriminant Analysis
method = 'slda'
Type: Classification
No Tuning Parameters
Stepwise Diagonal Linear Discriminant Analysis
method = 'sddaLDA'
Type: Classification
No Tuning Parameters
Support Vector Machines with Linear Kernel
method = 'svmLinear'
Type: Regression, Classification
Tuning Parameters: C (Cost)