borg_auto_check

Configures BORG to automatically validate train/test splits when using
supported ML frameworks. When enabled, BORG will intercept common
modeling functions and validate indices before training proceeds.

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

borg_auto_check function

<dl><dt>enable</dt>
<dd>Logical. If TRUE, enable auto-check mode. If FALSE, disable.</dd>
<dt>strict</dt>
<dd>Logical. If TRUE, throw errors on violations. If FALSE, warn.</dd>
<dt>verbose</dt>
<dd>Logical. If TRUE, print diagnostic messages.</dd></dl>

Arguments

Enable/Disable BORG Auto-Check Mode — borg_auto_check

<dl>

<dt>enable</dt>
<dd>Logical. If TRUE, enable auto-check mode. If FALSE, disable.</dd>


<dt>strict</dt>
<dd>Logical. If TRUE, throw errors on violations. If FALSE, warn.</dd>


<dt>verbose</dt>
<dd>Logical. If TRUE, print diagnostic messages.</dd>

</dl>

borg_auto_check: Enable/Disable BORG Auto-Check Mode

Description

Usage

Value

Arguments

Examples