# Gaussian Process

`method = 'gaussprLinear'`

**Type**: Regression, Classification

No Tuning Parameters

# Gaussian Process with Polynomial Kernel

`method = 'gaussprPoly'`

**Type**: Regression, Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Scale)

# Gaussian Process with Radial Basis Function Kernel

`method = 'gaussprRadial'`

**Type**: Regression, Classification

**Tuning Parameters**: `sigma`

(Sigma)

# Least Squares Support Vector Machine

`method = 'lssvmLinear'`

**Type**: Classification

No Tuning Parameters

# Least Squares Support Vector Machine with Polynomial Kernel

`method = 'lssvmPoly'`

**Type**: Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Scale)

# Least Squares Support Vector Machine with Radial Basis Function Kernel

`method = 'lssvmRadial'`

**Type**: Classification

**Tuning Parameters**: `sigma`

(Sigma)

# Oblique Random Forest

`method = 'ORFsvm'`

**Type**: Classification

**Tuning Parameters**: `mtry`

(#Randomly Selected Predictors)

# Partial Least Squares

`method = 'kernelpls'`

**Type**: Regression, Classification

**Tuning Parameters**: `ncomp`

(#Components)

# Polynomial Kernel Regularized Least Squares

`method = 'krlsPoly'`

**Type**: Regression

**Tuning Parameters**: `lambda`

(Regularization Parameter), `degree`

(Polynomial Degree)

# Radial Basis Function Kernel Regularized Least Squares

`method = 'krlsRadial'`

**Type**: Regression

**Tuning Parameters**: `lambda`

(Regularization Parameter), `sigma`

(Sigma)

# Relevance Vector Machines with Linear Kernel

`method = 'rvmLinear'`

**Type**: Regression

No Tuning Parameters

# Relevance Vector Machines with Polynomial Kernel

`method = 'rvmPoly'`

**Type**: Regression

**Tuning Parameters**: `scale`

(Scale), `degree`

(Polynomial Degree)

# Relevance Vector Machines with Radial Basis Function Kernel

`method = 'rvmRadial'`

**Type**: Regression

**Tuning Parameters**: `sigma`

(Sigma)

# Support Vector Machines with Class Weights

`method = 'svmRadialWeights'`

**Type**: Classification

**Tuning Parameters**: `sigma`

(Sigma), `C`

(Cost), `Weight`

(Weight)

# Support Vector Machines with Linear Kernel

`method = 'svmLinear'`

**Type**: Regression, Classification

**Tuning Parameters**: `C`

(Cost)

# Support Vector Machines with Polynomial Kernel

`method = 'svmPoly'`

**Type**: Regression, Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Cost), `C`

(Scale)

# Support Vector Machines with Radial Basis Function Kernel

`method = 'svmRadial'`

**Type**: Regression, Classification

**Tuning Parameters**: `sigma`

(Sigma), `C`

(Cost)

# Support Vector Machines with Radial Basis Function Kernel

`method = 'svmRadialCost'`

**Type**: Regression, Classification

**Tuning Parameters**: `C`

(Cost)