Generalized Linear Model with Stepwise Feature Selection
method = 'glmStepAIC'
Type: Regression, Classification
No Tuning Parameters
Linear Discriminant Analysis with Stepwise Feature Selection
method = 'stepLDA'
Type: Classification
Tuning Parameters: maxvar (Maximum #Variables), direction (Search Direction)
Linear Regression with Backwards Selection
method = 'leapBackward'
Type: Regression
Tuning Parameters: nvmax (Maximum Number of Predictors)
Linear Regression with Forward Selection
method = 'leapForward'
Type: Regression
Tuning Parameters: nvmax (Maximum Number of Predictors)
Linear Regression with Stepwise Selection
method = 'leapSeq'
Type: Regression
Tuning Parameters: nvmax (Maximum Number of Predictors)
Linear Regression with Stepwise Selection
method = 'lmStepAIC'
Type: Regression
No Tuning Parameters
Quadratic Discriminant Analysis with Stepwise Feature Selection
method = 'stepQDA'
Type: Classification
Tuning Parameters: maxvar (Maximum #Variables), direction (Search Direction)
Random Forest with Additional Feature Selection
method = 'Boruta'
Type: Regression, Classification
Tuning Parameters: mtry (#Randomly Selected Predictors)
Random k-Nearest Neighbors with Feature Selection
method = 'rknnBel'
Type: Classification, Regression
Tuning Parameters: k (#Neighbors), mtry (#Randomly Selected Predictors), d (#Features Dropped)
Ridge Regression with Variable Selection
method = 'foba'
Type: Regression
Tuning Parameters: k (#Variables Retained), lambda (L2 Penalty)
Stepwise Diagonal Linear Discriminant Analysis
method = 'sddaLDA'
Type: Classification
No Tuning Parameters
Stepwise Diagonal Quadratic Discriminant Analysis
method = 'sddaQDA'
Type: Classification
No Tuning Parameters