select_metrics

lifecycle::badge("maturing")
When reporting results, we might not want all
 the nested <code>tibble</code>s and process information columns.
 This function selects the evaluation metrics and model formulas only.
If an expected column is not in the <code>`results`</code> <code>tibble</code>, it is simply ignored.

Cross-validate one or multiple regression and classification 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 regression and classification (binary and multiclass).
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

select_metrics function

<dl><dt>results</dt>
<dd>Results <code>tibble</code>. E.g. from
<code>cross_validate()</code> or <code>evaluate()</code>.</dd>
<dt>include_definitions</dt>
<dd>Whether to include the <code>Dependent</code>,
<code>Fixed</code> and (possibly) <code>Random</code> and <code>HParams</code> columns. (Logical)</dd>
<dt>additional_includes</dt>
<dd>Names of additional columns to select. (Character)</dd></dl>

Arguments

Ludvig Renbo Olsen, <a href="/link/r-pkgs%40ludvigolsen.dk?package=cvms&version=1.7.0" data-mini-rdoc="cvms::r-pkgs@ludvigolsen.dk">r-pkgs@ludvigolsen.dk</a>

Author

Select columns with evaluation metrics and model definitions — select_metrics

<dl>

<dt>results</dt>
<dd>Results <code>tibble</code>. E.g. from
<code>cross_validate()</code> or <code>evaluate()</code>.</dd>


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


<dt>additional_includes</dt>
<dd>Names of additional columns to select. (Character)</dd>

</dl>

Ludvig Renbo Olsen, <a href='mailto:r-pkgs@ludvigolsen.dk'>r-pkgs@ludvigolsen.dk</a>

select_metrics: Select columns with evaluation metrics and model definitions

Description

Usage

Value

Arguments

Author