SVRl: Regression using Support Vector Machine with a linear kernel

In fdm2id: Data Mining and R Programming for Beginners

Regression using Support Vector Machine with a linear kernel

Description

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

Usage

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

Arguments

x	Predictor matrix.
y	Response vector.
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.

Value

The classification model.

See Also

svm, SVR

Examples

## Not run: 
require (datasets)
data (trees)
SVRl (trees [, -3], trees [, 3], cost = 1)

## End(Not run)

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.