Field note · June 12, 2024

Reader question — Features, and explaining what the model did

Feature engineering is where domain knowledge enters a model, and interpretation is where it leaves. Both are more valuable than algorithm selection.

1 min read ·Machine learning ·practice

Reader question on Features, and explaining what the model did, and the answer belongs somewhere more findable than an email.

Feature engineering is where domain knowledge enters a model, and interpretation is where it leaves. Both are more valuable than algorithm selection.

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 Validation that matches deployment and leads into When not to build a model, 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.