Module kernel_ridge 

Kernel Ridge Regression Kernel Ridge Regression

Kernel Ridge Regression (KRR) combines Ridge Regression with the kernel trick. It learns a linear function in the kernel-induced feature space that corresponds to a nonlinear function in the original space.

Algorithm

The KRR solution is: alpha = (K + lambda * I)^{-1} y Prediction: y_pred = K_test * alpha

Features

• Tikhonov regularized kernel regression
• Leave-one-out cross-validation in closed form (O(n^3) once)
• Multiple output support (each output trained independently)
• Support for all kernel types

Structs

KernelRidgeRegression

Kernel Ridge Regression