Explain how stopping training early affects model complexity.
Question: Describe how early stopping changes a model's bias and variance, and why it is often grouped with regularization methods.
Sample answer: If training is stopped before gradient descent fully fits the training set, the model usually becomes less sensitive to noise in the data. That lowers variance but leaves some training error uncorrected, so bias goes up. Since this limits how far the parameters can move during learning, early stopping acts like a form of regularization.
Key points:
- Stops training before full convergence
- Lowers variance
- Raises bias
- Functions like regularization
Rubric: The response must state that early stopping lowers variance while increasing bias. It should also explain that ending gradient descent early acts like a regularizer.
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