Regression using Support Vector Machine
This function builds a regression model using Support Vector Machine.
SVR(
x,
y,
gamma = 2^(-3:3),
cost = 2^(-3:3),
kernel = c("radial", "linear"),
epsilon = c(0.1, 0.5, 1),
params = NULL,
tune = FALSE,
...
)x |
Predictor |
y |
Response |
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. |
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) SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1) SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1) ## End(Not run)
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