# Bagged Logic Regression

`method = 'logicBag'`

**Type**: Regression, Classification

**Tuning Parameters**: `nleaves`

(Maximum Number of Leaves), `ntrees`

(Number of Trees)

# Boosted Linear Model

`method = 'bstLs'`

**Type**: Regression, Classification

**Tuning Parameters**: `mstop`

(# Boosting Iterations), `nu`

(Shrinkage)

# Cubist

`method = 'cubist'`

**Type**: Regression

**Tuning Parameters**: `committees`

(#Committees), `neighbors`

(#Instances)

# Elasticnet

`method = 'enet'`

**Type**: Regression

**Tuning Parameters**: `fraction`

(Fraction of Full Solution), `lambda`

(Weight Decay)

# glmnet

`method = 'glmnet'`

**Type**: Regression, Classification

**Tuning Parameters**: `alpha`

(Mixing Percentage), `lambda`

(Regularization Parameter)

# Independent Component Regression

`method = 'icr'`

**Type**: Regression

**Tuning Parameters**: `n.comp`

(#Components)

# Least Angle Regression

`method = 'lars'`

**Type**: Regression

**Tuning Parameters**: `fraction`

(Fraction)

# Least Angle Regression

`method = 'lars2'`

**Type**: Regression

**Tuning Parameters**: `step`

(#Steps)

# Linear Regression

`method = 'lm'`

**Type**: Regression

No Tuning Parameters

# 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

# Logic Regression

`method = 'logreg'`

**Type**: Regression, Classification

**Tuning Parameters**: `treesize`

(Maximum Number of Leaves), `ntrees`

(Number of Trees)

# Model Rules

`method = 'M5Rules'`

**Type**: Regression

**Tuning Parameters**: `pruned`

(Pruned), `smoothed`

(Smoothed)

# Model Tree

`method = 'M5'`

**Type**: Regression

**Tuning Parameters**: `pruned`

(Pruned), `smoothed`

(Smoothed), `rules`

(Rules)

# 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 Regression

`method = 'penalized'`

**Type**: Regression

**Tuning Parameters**: `lambda1`

(L1 Penalty), `lambda2`

(L2 Penalty)

# Principal Component Analysis

`method = 'pcr'`

**Type**: Regression

**Tuning Parameters**: `ncomp`

(#Components)

# Relaxed Lasso

`method = 'relaxo'`

**Type**: Regression

**Tuning Parameters**: `lambda`

(Penalty Parameter), `phi`

(Relaxation Parameter)

# Relevance Vector Machines with Linear Kernel

`method = 'rvmLinear'`

**Type**: Regression

No Tuning Parameters

# Ridge Regression

`method = 'ridge'`

**Type**: Regression

**Tuning Parameters**: `lambda`

(Weight Decay)

# Ridge Regression with Variable Selection

`method = 'foba'`

**Type**: Regression

**Tuning Parameters**: `k`

(#Variables Retained), `lambda`

(L2 Penalty)

# Robust Linear Model

`method = 'rlm'`

**Type**: Regression

No Tuning Parameters

# Sparse Partial Least Squares

`method = 'spls'`

**Type**: Regression, Classification

**Tuning Parameters**: `K`

(#Components), `eta`

(Threshold), `kappa`

(Kappa)

# Supervised Principal Component Analysis

`method = 'superpc'`

**Type**: Regression

**Tuning Parameters**: `threshold`

(Threshold), `n.components`

(#Components)

# Support Vector Machines with Linear Kernel

`method = 'svmLinear'`

**Type**: Regression, Classification

**Tuning Parameters**: `C`

(Cost)

# The lasso

`method = 'lasso'`

**Type**: Regression

**Tuning Parameters**: `fraction`

(Fraction of Full Solution)