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Why can weakening regularization help a model that is underfitting?
Question: Answer in one to three sentences: Why might lowering or removing penalties such as L1, L2, or dropout help when a model has high bias, and what drawback should you expect?
Sample answer: If the model is too constrained, reducing regularization can let it fit the training data more closely and lower bias. The main drawback is that the model may become more sensitive to the training set and overfit more easily, which increases variance.
Key points:
- Regularization can be lowered or removed.
- Bias decreases.
- Variance increases.
Rubric: The answer must state both effects: lower bias and higher variance.
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