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The most expensive ML mistake is building a model for a problem that didn’t need one. Before any training, you decide: is this a job for ML at all? This lesson gives you that filter.
The decision
Should this be ML?
Can you write the rule explicitly and it stays stable?
Yes → Use a rule — cheaper, debuggable, no drift.
No → keep going
Is there a good API/foundation model that already solves it?
Yes → Call the API — don’t train what you can rent.
No → keep going
Do you have labelled examples + a measurable target?
Yes → ML is a candidate — proceed to a baseline.
No → Get data first, or reframe the problem.
ML earns its complexity only when the pattern is real but too messy to write by hand, and you can measure whether you got it right.
Frame it as a learning problem
Target
What exactly are you predicting? One column, clearly defined.
Unit
One row = one what? (a user, an order, a session)
Signal
What inputs plausibly cause the target? Available at predict time?
Decision
What action does the prediction drive? If none, don’t build it.
⚠
The cardinal sin: no baseline
If you can’t state a dumb baseline (predict the mean, predict last value, a 3-line rule), you can’t tell whether your model is good. Always define the baseline first.
Takeaway
ML is for messy-but-measurable patterns you can’t hand-code. Define target, unit, signal, decision — and a baseline — before you train anything.