Field note · November 4, 2023

Cutting a lesson down — When not to build a model

The most valuable modelling skill is recognising the problems that do not need one, which is a large majority of the problems people bring you.

1 min read ·Machine learning ·practice

A note from writing When not to build a model, which took three passes to get to something short.

The most valuable modelling skill is recognising the problems that do not need one, which is a large majority of the problems people bring you.

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 Features, and explaining what the model did and leads into Choosing the form before the colours, 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.