Learn Before
Why enlarging a model can lower bias
When a system is underfitting, adding capacity can help it learn the training patterns more accurately. In a neural network, this usually means using more layers, more hidden units, or both. A larger model may also increase variance, so techniques such as weight decay, dropout, or early stopping are often used to keep that side effect under control.
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Related
Why enlarging a model can lower bias
Using More Data When Variance Is High
Why Increasing Model Size and Data Eventually Stops Helping
Methods to Reduce High Avoidable Bias
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
Learn After
Why would a team choose a larger neural network during development?
True or False: A larger model can reduce underfitting, but it may also become more sensitive to noise in the training data.
Fill in the blank: When a larger model begins to overfit, _____ is commonly used to reduce the resulting variance.
Match model adjustment terms with their descriptions.
Order the actions for handling high avoidable bias.
Which change most directly increases a neural network's capacity?
True or False: Increasing the width or depth of a neural network can reduce underfitting.
Fill in the blank: Increasing the model’s capacity can reduce _____ error caused by an overly simple model.
Match each concept with what usually happens when model capacity increases.
Steps for Increasing Model Capacity Safely
Explain why enlarging a model can help, and what new problem it may create.
Handling variance after enlarging a fraud-detection model
Why can a larger model lower avoidable bias?