Early Stopping and Its Effect on Model Fit
Question: How does early stopping work in training, and what does it do to bias and variance?
Sample answer: Early stopping means ending training before the optimization process fully converges, based on performance on a validation set. It acts like a form of regularization: it usually lowers variance but raises bias.
Key points:
- Training is stopped before full convergence
- Stopping is based on validation-set performance
- Lowers variance
- Raises bias
Rubric: The answer must state that training is halted early using validation performance and explain that this typically decreases variance while increasing bias.
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