fdm2id (version 1.0.1)

SVR: Regression using Support Vector Machine

Description

This function builds a regression model using Support Vector Machine.

Usage

SVR(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  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).

kernel

The kernel type.

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 (and behaves identically to) methodparameters rather than params, for consistency with SVM and with the calling convention used by performance/the internal protocol.* functions, which always pass a methodparameters argument.

graph

Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: SVR does not produce a plot.

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, SVRl, SVRr

Examples

if (FALSE) {
require (datasets)
data (trees)
SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1)
SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1)
}