.guard_ensure_levels

Converts character/logical columns to factors and aligns factor levels with
a training-time <code>levels_map</code>. Adds a dummy level when a column has only
one observed level so that downstream one-hot encoding retains a column.

internal

Prevents and detects information leakage in biomedical machine learning.
Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered),
guarded preprocessing (train-only imputation, normalization, filtering, feature selection),
cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests,
and duplicate detection.

Selcuk Korkmaz

bioLeak

Leakage-Safe Modeling and Auditing for Genomic and Clinical Data

.guard_ensure_levels function

<dl><dt>df</dt>
<dd>data.frame to normalize factor levels.</dd>
<dt>levels_map</dt>
<dd>optional named list of factor levels learned from training data.</dd>
<dt>dummy_prefix</dt>
<dd>prefix used when adding a dummy level to single-level factors.</dd></dl>

Arguments

Ensure consistent categorical levels for guarded preprocessing — .guard_ensure_levels

<dl>

<dt>df</dt>
<dd>data.frame to normalize factor levels.</dd>


<dt>levels_map</dt>
<dd>optional named list of factor levels learned from training data.</dd>


<dt>dummy_prefix</dt>
<dd>prefix used when adding a dummy level to single-level factors.</dd>

</dl>

.guard_ensure_levels: Ensure consistent categorical levels for guarded preprocessing

Description

Usage

Value

Arguments