tunable.krr_reg

Supplies a tibble of tunable arguments for <code>"krr_reg()"</code>.

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

tunable.krr_reg function

<dl><dt>x</dt>
<dd>A <code>"krr_reg"</code> model specification.</dd>
<dt>...</dt>
<dd>Not used; included for S3 method compatibility.</dd></dl>

Arguments

Expose tunable parameters for "krr_reg" — tunable.krr_reg

<dl>

<dt>x</dt>
<dd>A <code>"krr_reg"</code> model specification.</dd>


<dt>...</dt>
<dd>Not used; included for S3 method compatibility.</dd>

</dl>

tunable.krr_reg: Expose tunable parameters for `"krr_reg"`

Description

Usage

Value

Arguments