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Questions and Answers
Which method is helpful when the performance of your model shows significant variance based on your train-test split?
Which method is helpful when the performance of your model shows significant variance based on your train-test split?
- Partition evaluation
- Model calibration
- Hold-out validation
- Cross-validation (correct)
What is the final score in the cross-validation method?
What is the final score in the cross-validation method?
- The maximum score obtained on any partition
- The minimum score obtained on any partition
- The average of the scores obtained on all partitions (correct)
- The score obtained on the last partition
Does cross-validation exempt you from using a distinct validation set for model calibration?
Does cross-validation exempt you from using a distinct validation set for model calibration?
- Cross-validation replaces the need for model calibration
- Yes, cross-validation eliminates the need for a validation set
- Cross-validation can be used as a validation set
- No, cross-validation requires a distinct validation set (correct)
