Field note · December 3, 2024
Cutting a lesson down — 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.
A note from writing Features, and explaining what the model did, which took three passes to get to something short.
Feature engineering is where domain knowledge enters a model, and interpretation is where it leaves. Both are more valuable than algorithm selection.
The hard part of writing this was cutting it. The first version covered every case; the useful version covers the case you hit on a Tuesday and names the rest in a sentence. Completeness is a property of reference material, not of teaching material, and confusing the two produces something nobody finishes.
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.