error.krr

Computes the model error for kernel ridge regression (<code>"krr"</code>) objects.
Returns the mean squared error (MSE) between the observed responses
and the fitted values stored in the object.

Provides core computational operations in C++ via 'RcppArmadillo', enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with 'CVST' to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the 'tidymodels' ecosystem via the 'parsnip' model specification 'krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) <doi:10.1017/9781108627771>.

Kwan-Young Bak

FastKRR

Kernel Ridge Regression using 'RcppArmadillo'

Gyeongmin Kim

Seyoung Lee

Miyoung Jang

error.krr function

<dl><dt>x</dt>
<dd>An object of class <code>"krr"</code>, typically returned by
<code>fastkrr</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (ignored).</dd></dl>

Arguments

Compute Model Error for Kernel Ridge Regression Models — error.krr

<dl>

<dt>x</dt>
<dd>An object of class <code>"krr"</code>, typically returned by
<code>fastkrr</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (ignored).</dd>

</dl>

error.krr: Compute Model Error for Kernel Ridge Regression Models

Description

Usage

Value

Arguments

Details

See Also

Examples