run_detector

Provides utilities to detect common data leakage patterns including train/test
contamination, temporal leakage, and data duplication, enhancing model reliability and
reproducibility in machine learning workflows. Generates diagnostic reports and visual
summaries to support data validation. Methods based on best practices from Hastie,
Tibshirani, and Friedman (2009, ISBN:978-0387848570).

Cheryl Isabella

leakr

Data Leakage Detection Tools for Machine Learning

run_detector function

<dl><dt>detector</dt>
<dd>A detector object</dd>
<dt>data</dt>
<dd>Data frame to analyze</dd>
<dt>split</dt>
<dd>Split vector indicating train/test assignment (optional)</dd>
<dt>id</dt>
<dd>Optional ID column name</dd>
<dt>config</dt>
<dd>Optional configuration list</dd></dl>

Arguments

Run a detector on data — run_detector

<dl>

<dt>detector</dt>
<dd>A detector object</dd>


<dt>data</dt>
<dd>Data frame to analyze</dd>


<dt>split</dt>
<dd>Split vector indicating train/test assignment (optional)</dd>


<dt>id</dt>
<dd>Optional ID column name</dd>


<dt>config</dt>
<dd>Optional configuration list</dd>

</dl>

run_detector: Run a detector on data

Description

Usage

Value

Arguments