Question: Explain why a cat classifier with 1% estimated bias and 10% estimated variance is considered to have "high variance," and describe the relationship between its performance on the training set versus the development set.

Sample answer: A classifier with 1% bias and 10% variance has "high variance" because the gap between its training error and development set error is large (10%). This indicates that while the classifier has very low training error (approx 1%), it is failing to generalize well to the dev set (11% error). This phenomenon, where the model performs exceptionally well on the training data but poorly on unseen data, is known as overfitting.

Key points:

• Variance is estimated at 10% (the gap between dev and training error).
• The classifier has very low training error, implying low bias (1%).
• The model fails to generalize to the dev set.
• This condition is referred to as overfitting.

Rubric: The response should explicitly define the gap between training and dev error as the variance (10%), acknowledge the low training error indicating low bias (1%), and correctly identify the failure to generalize as overfitting.