fdm2id (version 1.0.1)

SVRr: Regression using Support Vector Machine with a radial kernel

Description

This function builds a regression model using Support Vector Machine with a radial kernel.

Usage

SVRr(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Value

The classification model.

Arguments

x

Predictor matrix.

y

Response vector.

gamma

The gamma parameter (if a vector, cross-over validation is used to chose the best size).

cost

The cost parameter (if a vector, cross-over validation is used to chose the best size).

epsilon

The epsilon parameter (if a vector, cross-over validation is used to chose the best size).

nfolds

The number of folds of the cross-validation a method runs to choose its hyperparameters. Only used when there is something to choose, i.e. when one of them is given as a vector. Lower it to fit faster, at the cost of a noisier choice.

tune

If true, the function returns parameters instead of a classification model.

methodparameters

Object containing the parameters. If given, it replaces epsilon, gamma and cost. Named to match SVR (see there).

graph

Whether the method draws the graphic that goes with its tuning (the cross-validation curve, typically). Methods that have no such graphic accept the argument and ignore it.

seed

A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.

...

Other arguments.

See Also

svm, SVR

Examples

if (FALSE) {
require (datasets)
data (trees)
SVRr (trees [, -3], trees [, 3], gamma = 1, cost = 1)
}