Data Leakage
Data leakage is reading tomorrow's newspaper to answer today's quiz: the model secretly gets information it will not have when it must actually predict. Offline scores look amazing, production collapses. This topic catalogs the four ways it sneaks in and the discipline that keeps it out.
01.The Problem: Great in the Notebook, Disaster in Production
Your team builds a churn predictor.
In the notebook it scores 99% accuracy. Champagne is opened.
Two weeks after launch, it performs barely better than guessing.
What happened?
The notebook number was earned with information the live model never has.
That cheat has a name: data leakage — the silent killer of ML projects, and the most common reason a prototype "worked in the notebook but not in production."
How Leakage Breaks Models
How Leakage Breaks Models
Information that would not be available at prediction time leaks into features or fitted transforms, producing great offline metrics that fail in production.
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