Early Stopping Quiz

Answers and explanations appear as you take the quiz.

1. What signal does early stopping primarily monitor to decide when to halt?
   • Validation-set performance
   • Training-set loss
   • GPU temperature
   • Number of model parameters
2. What does the 'patience' parameter control?
   • The maximum learning rate
   • How many epochs to wait without improvement before stopping
   • The batch size
   • The number of validation folds
3. After early stopping triggers, which weights should you typically keep?
   • The weights from the final epoch
   • The randomly initialized weights
   • The weights from the best validation epoch
   • The average of all epochs
4. Why can early stopping act as a form of regularization?
   • It adds a fixed L2 penalty to every weight
   • It guarantees lower test error on every dataset
   • It prevents the model from seeing training examples
   • It limits how long optimization proceeds and can reduce fitting to training-specific patterns
5. The typical shape of the validation-error curve during training is best described as:
   • A U-shape that falls then rises
   • Monotonically decreasing
   • A flat line
   • Monotonically increasing
6. What does the 'min_delta' threshold typically specify?
   • The minimum number of layers
   • The minimum improvement that counts as progress
   • The minimum batch size
   • The minimum dataset size
7. What does patience allow an early-stopping rule to do?
   • Continue training forever regardless of validation results
   • Change the model architecture after every batch
   • Wait through a set number of validation checks without qualifying improvement before stopping
   • Use the test set to select the final epoch
8. Why should the test set generally remain outside the early-stopping loop?
   • The test set cannot contain labels
   • Training libraries cannot load test data
   • Early stopping requires the test set to be empty
   • Repeatedly selecting by test performance would make it part of model selection