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.
Evaluación·20/06/2026·2 min
Your eval dropped from 90 to 89%: real regression or noise?
A new model scores 89.4% where the old one scored 90.0% on 1,000 examples. It looks like a regression. At that sample size it is noise. How to tell them apart before blocking a deploy.
Read articleEvaluación·08/05/2026·2 min
How to evaluate whether an ML model actually works
A model with 95% accuracy can be completely useless. A model that looks worse in validation may be the one that actually works in production. What separates honest evaluation from the kind that inflates results.
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