Notes on machine learning

Technical notes written while learning: honest evaluation, data leakage, deployment, LLM applications and the mistakes behind each lesson. They come from the projects on this site, with the code one click away.

Datos·13/06/2026·2 min

Leakage between train and test: the mistake that inflates every metric

A row that appears in both training and test makes the model look better than it is. It is easy to introduce by accident and hard to see by eye. Where it creeps in and how to catch it before you trust your numbers.

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