SVM: Classification using Support Vector Machine

In fdm2id: Data Mining and R Programming for Beginners

Classification using Support Vector Machine

Description

This function builds a classification model using Support Vector Machine.

Usage

SVM(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
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.
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 gamma and cost.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: SVM 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.

Value

The classification model.

See Also

svm, SVMl, SVMr

Examples

## Not run: 
require (datasets)
data (iris)
SVM (iris [, -5], iris [, 5], kernel = "linear", cost = 1)
SVM (iris [, -5], iris [, 5], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)

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