select_metrics

Results tibble from <code><a rd-options="" href="/link/cross_validate?package=cvms&version=0.2.0" data-mini-rdoc="cvms::cross_validate">cross_validate</a>()</code> or <code><a rd-options="" href="/link/validate?package=cvms&version=0.2.0" data-mini-rdoc="cvms::validate">validate</a>()</code>.

results

Whether to include the <code>Dependent</code>,
<code>Fixed</code> and (possibly) <code>Random</code> columns. (Logical)

include_definitions

Names of additional columns to select. (Character)

additional_includes

When reporting results, we might not want all
 the nested tibbles and process information columns.
 This function selects the evaluation metrics and model formulas only.

Cross-validate one or multiple regression models and get relevant evaluation
metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation.
Alternatively, evaluate predictions from an external model.
Currently supports linear regression, logistic regression and
(some functions only) multiclass classification.
Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Ludvig Renbo Olsen

cvms

Cross-Validation for Model Selection

Benjamin Hugh Zachariae

select_metrics function

Results tibble from <code><a rd-options='' href='cross_validate'>cross_validate</a>()</code> or <code><a rd-options='' href='validate'>validate</a>()</code>.

select_metrics: Select columns with evaluation metrics and model definitions.

Description

Usage

Arguments

Value

Details