Evaluate it on a held-out set excluded from training: Which evaluation design best distinguishes learning from

Held-out cases must remain separate from training so evaluation reflects performance on unseen applications.

The question

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1. Evaluate it on a held-out set excluded from training. ✓

   A held-out set provides separated evidence about performance on cases not used to fit the model.
2. Ask caseworkers whether predictions appear reasonable during routine processing.

   Expert review can supplement evaluation, but informal impressions do not replace separated held-out testing for predictive performance.
3. Use additional historical cases after mixing them into the training set.

   Mixing additional cases into training prevents those cases from serving as independent evidence of generalization.
4. Evaluate the model on the same cases used for training.

   Training-case evaluation can reflect memorization and does not provide an independent estimate of performance on new benefit applications.

How to remember it

Held-out cases must remain separate from training so evaluation reflects performance on unseen applications.

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