Evaluate once on a held-out December-like set: Which evidence | AIGP
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Evaluate once on a held-out December-like set: Which evidence would most directly address whether performance

AIGP Understanding How to Govern AI Development Medium

Evaluate the finalized model on a held-out, December-like set to test generalization beyond tuning data.

The question

A retailer trained a demand model on January–October sales and used November records for tuning. The team now reports strong November accuracy and seeks approval for December deployment. Which evidence would most directly address whether performance generalizes beyond tuning data?

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  1. Report November accuracy with additional decimal places
    More precise reporting does not change the fact that November records influenced tuning and are not held out.
  2. Ask planners whether November forecasts seemed reasonable
    Planner judgments may supplement evaluation, but cannot replace a held-out test of performance under deployment-like conditions.
  3. Evaluate once on a held-out December-like set ✓
    A separate held-out set resembling December deployment conditions provides independent evidence after training and tuning are complete.
  4. Tune the model further on November records
    Further tuning on November records increases exposure to the same data and cannot provide an independent generalization check.
The trap
Check whether any reported evaluation cases influenced model fitting or tuning; if so, they are not held out.

How to remember it

Evaluate the finalized model on a held-out, December-like set to test generalization beyond tuning data.

How many of these would you get right?

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