Regression using Support Vector Machine with a linear kernel
This function builds a regression model using Support Vector Machine with a linear kernel.
SVRl( x, y, cost = 2^(-3:3), epsilon = c(0.1, 0.5, 1), params = NULL, tune = FALSE, ... )
x |
Predictor |
y |
Response |
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 |
tune |
If true, the function returns paramters instead of a classification model. |
... |
Other arguments. |
The classification model.
## Not run: require (datasets) data (trees) SVRl (trees [, -3], trees [, 3], cost = 1) ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.