Field note · July 30, 2023

Reader question — Validation that matches deployment

Cross-validation is not one technique. Choosing the wrong variant is how models get shipped with an offline score that has no relationship to what happens next.

1 min read ·Machine learning ·practice

Reader question on Validation that matches deployment, and the answer belongs somewhere more findable than an email.

Cross-validation is not one technique. Choosing the wrong variant is how models get shipped with an offline score that has no relationship to what happens next.

The question was, roughly, "when does this stop applying?" — which is the right question and the one lessons routinely fail to answer. Every technique has a range of validity, and stating it is what separates a lesson from a recipe.

It sits in the Modelling, honestly module. The exercise is a thinking one rather than a query one — some of this material is about deciding what to compute, and that does not need a keyboard.

Ordering matters here more than in most courses. It follows Frame the problem, then beat a stupid baseline and leads into Features, and explaining what the model did, and reading it out of sequence mostly works but costs you the setup.

Free means free, and it also means we can rewrite it whenever it is wrong. No edition, no errata PDF, nothing to repurchase. The whole course is 36 lessons and the fixes land the day we find them.