SVR: Regression using Support Vector Machine

In fdm2id: Data Mining and R Programming for Beginners

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),
  params = NULL,
  tune = FALSE,
  ...
)

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).
params	Object containing the parameters. If given, it replaces epsilon, gamma and cost.
tune	If true, the function returns paramters instead of a classification model.
...	Other arguments.

Value

The classification model.

See Also

svm, SVRl, SVRr

Examples

## Not run: 
require (datasets)
data (trees)
SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1)
SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)

fdm2id documentation built on July 9, 2023, 6:05 p.m.