### Elastic Net

`models = "ENet"`

Type: Classification, Regression

Tuning parameter defaults:

`lambda = 1`

- The quadratic penalty parameter.

Required packages: `elasticnet`

### K Nearest Neighbors

`models = "KNN"`

Type: Classification, Regression

Tuning parameter defaults:

`k = 10`

- The number of nearest neighbors to consider.

Required packages: `class`

### Lasso

`models = "Lasso"`

Tuning parameter defaults:

Type: Classification, Regression

Required packages: `lars`

### Least Angle Regression

`models = "LARs"`

Tuning parameter defaults:

Type: Classification, Regression

Required packages: `lars`

### Neural Networks

`models = "NNet"`

Type: Classification, Regression

`size = 2`

- The number of neurons in the hidden layer.

`decay = 0`

- Weight decay. Controls the regularization of the cost function.

Required packages: `nnet`

### Partial Least Squares Linear Discriminant Analysis

`models = "PLSLDA"`

Type: Classification

Tuning parameter defaults:

`ncomp = min(floor(n/nfolds), p, 100))`

- The number of components.
`n`

is the number of observations. `nfolds`

is the number of folds. `p`

is the number of parameters.

### Principal Components Regression

`models = "PCR"`

Type: Classification, Regression

Required packages: `pls`

### Random Forest

`models = "Forest"`

Type: Classification, Regression

Tuning parameter defaults:

`mtry = ifelse(classify, max(floor(p/3), 1), floor(sqrt(p)))`

- Number of variables randomly sampled as candidates at each split.
`p`

is the number of descriptors.

Required packages: `randomForest`

### Recursive Partitioning (RPart)

`models = "RPart"`

Type: Classification, Regression

Tuning parameter defaults:

`cp = .01`

- The overall R-squared must increase by cp with each split.

Required packages: `rpart`

### Recursive Partitioning (Tree)

`models = "Tree"`

Type: Classification, Regression

Required packages: `tree`

### Ridge Regression

`models = "Ridge"`

Type: Classification, Regression

Tuning parameter defaults:

Required packages: `MASS`

### Support Vector Machines

`models = "SVM"`

Type: Classification, Regression

#### Notes

Depending on whether y is binary or continuous, C-classification or eps-regression is used.

Tuning parameter defaults:

`gamma = 1`

- The gamma controls how local/flexible the fit is (the larger, the more local the fit)

`cost = 1`

- For C-classification, controls the number and the severity of the violations of the margin in maximal margin classification. The larger C is, the more “budget” for violations.

`epsilon = .01`

- For epsilon-Regression, epsilon controls what is comparable to the size of the margin in C-classification

Required packages: `e1071`