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Using Less Regularization to Ease Underfitting
When a model is underfitting, lowering or removing regularization methods such as L2 penalty, L1 penalty, or dropout can reduce avoidable bias. The trade-off is that less regularization usually raises variance, so the model may fit the training data better while becoming more sensitive to noise.
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Using Error Review to Guide Feature Design
Using Less Regularization to Ease Underfitting
Which change is most likely to reduce avoidable bias when a model is underfitting?
More training data and avoidable bias
A way to lower avoidable bias is to reduce or eliminate ____.
Match each action with its main effect or purpose.
Order the response to weak training-set performance.
How can four bias-reduction tactics help when a model underfits?
Fixing poor training-set performance in a model
Why can weakening regularization help a model that is underfitting?
Which choice best applies error analysis to reduce avoidable bias?
Changing a model’s architecture can affect both bias and variance.
Learn After
What is the main trade-off when you relax regularization to reduce avoidable bias?
True or False: Lowering a strong regularization setting, such as dropout, can reduce avoidable bias but often increases variance.
Less regularization can lower bias but raise _____.
Match each training technique to the description of what it does.
Put the steps in the right order for deciding whether to weaken regularization when bias is too high.
Weighing regularization when a model is underfitting
When training error stays high but the validation gap is small, should dropout be reduced?
Why can less regularization lower bias but raise variance?
Which regularization method is explicitly identified as something that can be reduced to lower avoidable bias?
True or False: Removing regularization lowers variance without changing bias.