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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