Data Leakage

Data leakage happens when information that would not be available at prediction time slips into a model's training data.

The model effectively sees the answer in advance, so it scores far higher in testing than it ever will in production. It is one of the most common reasons a promising machine learning project quietly fails after deployment.

What this topic covers

Foundations — Start here to understand what data leakage actually is: how information that should stay hidden during training quietly reaches the model, and why the inflated accuracy it produces is so easy to miss until production.
Implementation — These guides show you how to catch leakage before it ships: structuring pipelines so preprocessing never sees the test set, validating splits, and the trade-offs between strict isolation and convenient feature engineering.
What's changing — Leakage is no longer just a pipeline bug.
Risks & limits — Before you trust a model's reported accuracy, consider what leakage hides: results that quietly mislead users, the blurry line between an honest mistake and convenient omission, and who bears the cost when an inflated system fails in the real world.

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