summary.borg_cv

Automatically detects and enforces valid model evaluation by identifying
information reuse between training and evaluation data. Guards against data leakage,
look-ahead bias, and invalid cross-validation schemes that inflate performance estimates.
Supports temporal, spatial, and grouped evaluation structures.
Based on evaluation principles described in
Roberts et al. (2017) <doi:10.1111/ecog.02881>,
Kaufman et al. (2012) <doi:10.1145/2382577.2382579>, and
Kapoor & Narayanan (2023) <doi:10.1016/j.patter.2023.100804>.

Gilles Colling

BORG

Bounded Outcome Risk Guard for Model Evaluation

summary.borg_cv function

<dl><dt>object</dt>
<dd>A <code>borg_cv</code> object from <code>borg_cv</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Summarize BORG Cross-Validation — summary.borg_cv

<dl>

<dt>object</dt>
<dd>A <code>borg_cv</code> object from <code>borg_cv</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (currently unused).</dd>

</dl>

summary.borg_cv: Summarize BORG Cross-Validation

Description

Usage

Value

Arguments