Description Usage Arguments Details Value References See Also Examples
Fit kernel ridge regression, i.e. reproducing kernel Hilbert space regression.
1 |
x |
a matrix of predictors. |
y |
a vector of response. |
group |
an optional vector of the same length as |
krr
minimizes the sum of squared loss plus a penalty term on the
squared norm of the regression function. Gaussian kernel is used.
Tuning parameters are chosen by minimizing the leave-one-out cross validated
mean squared error. See the mathematical formulation in the reference.
An object of class krr
.
https://arxiv.org/abs/1606.01472
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