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"),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

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.
methodparameters	Object containing the parameters. If given, it replaces gamma and cost.
tune	If true, the function returns paramters instead of a classification model.
...	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 July 9, 2023, 6:05 p.m.