data leakage detection lab

The problem

Machine learning models often report suspiciously high performance that collapses in production. This usually stems from data leakage, where information from the future or the test set accidentally contaminates the training process.

What it does

• Identifies future information leakage by auditing feature timestamps against label timestamps.
• Detects preprocessing contamination where scalers, encoders, or imputers are fit on the full dataset before splitting.
• Spots sample and group leakage where duplicate entities or near-duplicate records straddle the train-test boundary.
• Exposes accidental hindsight by flagging features derived from revised, updated, or backfilled data.
• Performs adversarial validation to determine if training and test sets are statistically distinguishable.

Frameworks & tools

Designed for Python-based data science stacks including scikit-learn, pandas, NumPy, and XGBoost/LightGBM. Works with Jupyter notebooks and automated ML pipelines.

Why this beats prompting it yourself

Standard LLMs often miss subtle leakage patterns like target encoding inside cross-validation or group-based dependencies. This skill uses specific diagnostic playbooks and adversarial tests to catch technical debt that manual reviews overlook.

Use cases

• Audit a Jupyter notebook before moving a model to a production environment.
• Debug why a model achieved 0.99 AUC on real-world noisy data.
• Verify that a temporal split correctly handles rolling windows and lag features.
• Ensure multi-user datasets don't leak information via group identity.

Known limitations

Requires access to the data pipeline code and feature engineering logic. It cannot detect leakage in pre-compiled black-box models without seeing the training workflow.