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ML SPLIT INTEGRITY FORENSICS

ML Dataset Leakage & Drift Lab

Before trusting an evaluation, compare train and test splits for copied rows, shared entities, group leakage, time travel, target proxies and feature drift.

Reviewed 2026-07-09

Data
Exact + near overlapPSI / KS / JS driftTarget proxy screenQuarantine CSV

WHAT THIS TOOL DOES

ML Dataset Leakage & Drift Lab: inputs, outputs and verification

Use it whentrain test data leakage checker, dataset contamination checker, machine learning data drift detector
You getA browser-only split-integrity packet with exact and near-overlap evidence, group/time leakage checks, PSI/KS/Jensen-Shannon drift, target-proxy
Verify withJSON proof receipt, CSV, visual artifact, Markdown generated from visible local input

Find leakage before it inflates your model score.

Load the synthetic case or paste two de-identified CSV splits. Choose optional ID, target, group and time columns, then run one audit.

Result appears here after you run the split-integrity audit.

WHY THIS IS DIFFERENT

Leakage and drift are not the same failure.

Copied examples can inflate evaluation scores while distribution shift can break production behavior. This lab reports overlap, split-policy violations, proxy signals and drift separately so the remediation is specific.

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