Why does reducing regularization increase variance even as it reduces avoidable bias?
Question: In one to three sentences, explain why reducing or eliminating regularization increases variance even though it reduces avoidable bias.
Sample answer: Regularization limits how closely a model can fit the training data, which helps control variance. When regularization is reduced, the model gains more freedom to fit training data details and noise, lowering avoidable bias but making it more prone to overfitting, which increases variance.
Key points:
- Regularization limits model flexibility
- Reducing it allows closer fit to training data
- Closer fit lowers avoidable bias
- Closer fit also increases sensitivity to noise, raising variance
Rubric: Full credit for mentioning that regularization limits model flexibility/fit to training data, and that removing it allows closer fitting (lowering bias) at the cost of overfitting to noise (raising variance).
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