svr
fun <T> svr(x: Array<T>, y: DoubleArray, kernel: MercerKernel<T>, eps: Double, C: Double, tol: Double = 0.001): KernelMachine<T>
Support vector regression. Like SVM for classification, the model produced by SVR depends only on a subset of the training data, because the cost function ignores any training data close to the model prediction (within a threshold).
Return
SVR model.
Parameters
x
training data.
y
response variable.
kernel
the kernel function.
eps
the loss function error threshold.
C
the soft margin penalty parameter.
tol
the tolerance of convergence test.