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Ways to Reduce High Variance
When a model has high variance, common remedies include collecting more training examples, adding regularization, using early stopping, simplifying the feature set, or changing the model so it is less flexible. Another option is to adjust the input representation or architecture if error analysis shows that the current setup is too sensitive to the training data.
Reducing model size can also lower variance, but it should be treated carefully. If computation is not the main limitation, regularization often gives better overall classification performance than simply making the network smaller.
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Ways to Reduce High Variance
What should you do when avoidable bias is high?
True or false: When a model has high variance, adding more training examples is often a useful remedy.
For persistent underfitting, increase the _____ of the model.
Match each learning problem or model change with the usual remedy or meaning.
Order the steps for applying a simple model-fix rule.
How the diagnosis determines the fix for bias and variance
Select the right remedy for bias and variance in two projects.
State the recommended fix for each training problem.
Which option applies the rule of thumb correctly to both cases?
Bias and variance call for the same fix
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Regularization Lowers Variance but Raises Bias
Stopping Training When Validation Performance Stops Improving
Feature Selection and the Variance–Bias Trade-off
Using a Smaller Model to Control Variance
What is usually the first choice for reducing variance when enough data are available?
Regularization can lower variance even if it increases bias.
Stopping training early based on _____-set error can reduce variance.
Match each variance-reduction tactic with the main consideration that goes with it.
Order the steps for deciding how to reduce overfitting in a model.
Why should shrinking a model be treated as a cautious option when variance is high?
Prefer regularization when model cost is acceptable
How can error analysis suggest new input features?
Which choice describes early stopping for a model with high variance?
Installing a smaller set of features will always reduce variance without changing bias.