fdm2id: SVRr – R documentation

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SVRr

Regression using Support Vector Machine with a radial kernel

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

SVRr(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

`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).
`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.

The classification model.

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

## End(Not run)

Data Mining and R Programming for Beginners

v0.9.5

GPL-3

Authors

Alexandre Blansché [aut, cre]

Initial release