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.
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 articleHow 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.
Read articleShall we connect?
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