Case context: A data science team trains several models, compares their results on a held-out test set, and then uses those same test results to choose the final model and tune its hyperparameters.

Question: What is wrong with this workflow, and what should the test set be used for instead?

Sample answer: The team is using the test set for decisions it should not influence. Model selection and hyperparameter tuning should be based on training or validation data, while the test set should be saved for a final, unbiased evaluation after all choices are finished.

Key points:

• Do not use the test set to choose among models.
• Do not use the test set to tune parameters.
• Use the test set only for final performance evaluation.

Rubric: The response must identify both improper uses of the test set—model selection and parameter tuning—and explain that the test set should be reserved for final evaluation.