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4 cards from this section. Quick, good the night before a test.
What does high variance indicate in a model?
What happens when a model learns noise?
Define overfitting.
What happens when a model memorizes training data?
What does high variance imply in a model?
Think of models that perform well on training data but poorly elsewhere.
True or False: High bias leads to overfitting.
Consider if the model is too simple.
Imagine you are developing a model for fraud detection. Describe the impact of high variance on model performance and suggest methods to mitigate this issue.
Reflect on what happens when a model learns too many rules from the training set.
Consider a scenario where you have a linear regression model that is consistently underperforming. What steps can you take to evaluate whether the model is suffering from bias or variance?
Analyze what happens during training versus testing.
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