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Analyze the trade-offs of early stopping in gradient descent.
Question: Discuss the effect of early stopping on a model's bias and variance, and explain why some authors refer to it as a regularization technique.
Sample answer: Early stopping halts gradient descent early based on the dev-set error. By preventing the model from perfectly fitting the training data, it reduces variance but inherently increases bias. Because it constrains the learning process, it behaves much like regularization, leading some authors to classify it as a regularization technique.
Key points:
- Halts gradient descent based on dev-set error
- Reduces variance
- Increases bias
- Behaves like a regularization technique
Rubric: The response must mention that early stopping reduces variance while increasing bias. It should also state that stopping gradient descent early based on dev-set error behaves like regularization.
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Related
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