# Gaussian Process with Polynomial Kernel

`method = 'gaussprPoly'`

**Type**: Regression, Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Scale)

# Least Squares Support Vector Machine with Polynomial Kernel

`method = 'lssvmPoly'`

**Type**: Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Scale)

# Penalized Discriminant Analysis

`method = 'pda'`

**Type**: Classification

**Tuning Parameters**: `lambda`

(Shrinkage Penalty Coefficient)

# Penalized Discriminant Analysis

`method = 'pda2'`

**Type**: Classification

**Tuning Parameters**: `df`

(Degrees of Freedom)

# Polynomial Kernel Regularized Least Squares

`method = 'krlsPoly'`

**Type**: Regression

**Tuning Parameters**: `lambda`

(Regularization Parameter), `degree`

(Polynomial Degree)

# Quadratic Discriminant Analysis

`method = 'qda'`

**Type**: Classification

No Tuning Parameters

# Quadratic Discriminant Analysis with Stepwise Feature Selection

`method = 'stepQDA'`

**Type**: Classification

**Tuning Parameters**: `maxvar`

(Maximum #Variables), `direction`

(Search Direction)

# Regularized Discriminant Analysis

`method = 'rda'`

**Type**: Classification

**Tuning Parameters**: `gamma`

(Gamma), `lambda`

(Lambda)

# Relevance Vector Machines with Polynomial Kernel

`method = 'rvmPoly'`

**Type**: Regression

**Tuning Parameters**: `scale`

(Scale), `degree`

(Polynomial Degree)

# Robust Quadratic Discriminant Analysis

`method = 'QdaCov'`

**Type**: Classification

No Tuning Parameters

# Stepwise Diagonal Quadratic Discriminant Analysis

`method = 'sddaQDA'`

**Type**: Classification

No Tuning Parameters

# Support Vector Machines with Polynomial Kernel

`method = 'svmPoly'`

**Type**: Regression, Classification

**Tuning Parameters**: `degree`

(Polynomial Degree), `scale`

(Cost), `C`

(Scale)