Final Machine-Learning Project

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

The practice target is 80%. You can retry as often as you like, and every lesson stays accessible.

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1. Which explanation matches frame a prediction and baseline?A. Version the data split, preprocessing, code, parameters, and random seeds.B. Select on the same validation protocol, then assess the chosen candidate on an untouched test period.C. Report data scope, expected benefit, error costs, subgroup results, monitoring, and rollback criteria.D. Specify the decision time, label, eligible inputs, baseline, and cost of mistakes.

2. Which explanation matches split and train reproducibly?A. Specify the decision time, label, eligible inputs, baseline, and cost of mistakes.B. Select on the same validation protocol, then assess the chosen candidate on an untouched test period.C. Version the data split, preprocessing, code, parameters, and random seeds.D. Report data scope, expected benefit, error costs, subgroup results, monitoring, and rollback criteria.

3. Which explanation matches compare candidates on honest data?A. Specify the decision time, label, eligible inputs, baseline, and cost of mistakes.B. Select on the same validation protocol, then assess the chosen candidate on an untouched test period.C. Version the data split, preprocessing, code, parameters, and random seeds.D. Report data scope, expected benefit, error costs, subgroup results, monitoring, and rollback criteria.

4. For split and train reproducibly, which result or action fits this practical case: The team needs another person to repeat its training run.A. Rebuild the validation score from a clean environment using the saved recipe.B. Write a yes-or-no ten-minute-late target and a route-frequency baseline.C. Report both score uncertainty and important error categories.D. Present a deployment plan with human review for uncertain warnings.

5. For report limits and a deployment plan, which result or action fits this practical case: Operations asks whether the final model is safe to try.A. Write a yes-or-no ten-minute-late target and a route-frequency baseline.B. Rebuild the validation score from a clean environment using the saved recipe.C. Report both score uncertainty and important error categories.D. Present a deployment plan with human review for uncertain warnings.

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Final Machine-Learning Project

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