Reliable Model Evaluation

Five questions. Choose one answer for each, then submit to see your score and explanations.

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1. Which explanation matches confusion matrix for real decisions?A. A confusion matrix counts true positives, false positives, true negatives, and false negatives for a chosen threshold.B. Cross-validation must keep related groups together; time-based tasks should preserve chronology.C. Estimated scores vary with sample size and sampling; intervals express uncertainty under assumptions.D. Choose candidates with validation data, then compare final outcomes on the same untouched test set.

2. Which explanation matches cross-validation with groups and time?A. A confusion matrix counts true positives, false positives, true negatives, and false negatives for a chosen threshold.B. Estimated scores vary with sample size and sampling; intervals express uncertainty under assumptions.C. Choose candidates with validation data, then compare final outcomes on the same untouched test set.D. Cross-validation must keep related groups together; time-based tasks should preserve chronology.

3. Which explanation matches uncertainty in an estimated score?A. A confusion matrix counts true positives, false positives, true negatives, and false negatives for a chosen threshold.B. Cross-validation must keep related groups together; time-based tasks should preserve chronology.C. Estimated scores vary with sample size and sampling; intervals express uncertainty under assumptions.D. Choose candidates with validation data, then compare final outcomes on the same untouched test set.

4. For cross-validation with groups and time, which result or action fits this practical case: Several records share a driver and repeated route conditions.A. From 20 actual late trips, count how many were flagged and how many were missed.B. Assign each driver or future period to a held-out fold without leaking duplicates.C. Compare score intervals from repeated valid splits or a suitable resampling method.D. Report the difference alongside its uncertainty and the cases where predictions disagree.

5. For compare two models without test-set tuning, which result or action fits this practical case: Two models differ by one percentage point on the held-out period.A. Report the difference alongside its uncertainty and the cases where predictions disagree.B. From 20 actual late trips, count how many were flagged and how many were missed.C. Assign each driver or future period to a held-out fold without leaking duplicates.D. Compare score intervals from repeated valid splits or a suitable resampling method.

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Reliable Model Evaluation

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