get_cv_sl_folds

Get a numeric vector with cross-validation fold IDs from CV.SuperLearner

Calculate point estimates of and valid confidence intervals for
nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
using flexible estimators of the underlying regression functions. For more information
about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Brian D. Williamson

vimp

Perform Inference on Algorithm-Agnostic Variable Importance

Jean Feng

Charlie Wolock

Noah Simon

Marco Carone

get_cv_sl_folds function

<dl><dt>cv_sl_folds</dt>
<dd>The folds from a call to <code>CV.SuperLearner</code>; a list.</dd></dl>

Arguments

Get a numeric vector with cross-validation fold IDs from CV.SuperLearner — get_cv_sl_folds

<dl>

<dt>cv_sl_folds</dt>
<dd>The folds from a call to <code>CV.SuperLearner</code>; a list.</dd>

</dl>

get_cv_sl_folds: Get a numeric vector with cross-validation fold IDs from CV.SuperLearner

Description

Usage

Value

Arguments