Question: Based on Andrew Ng's Machine Learning Yearning course, explain why standard overfitting and underfitting terminology is difficult to apply to a classifier with 15% estimated bias and 15% estimated variance. Detail its performance on both training and dev sets in your explanation.

Sample answer: Standard overfitting and underfitting terminology is difficult to apply because a classifier with 15% estimated bias and 15% estimated variance is simultaneously overfitting and underfitting. It underfits because it performs poorly on the training set, indicating high bias. At the same time, it overfits because its performance on the dev set is even worse, showing high variance. Since both phenomena occur at the same time, neither term alone suffices to describe the classifier's state.

Key points:

• The classifier is simultaneously overfitting and underfitting.
• Poor performance on the training set indicates high bias.
• Even worse performance on the dev set indicates high variance.

Rubric: The response must explain that the classifier is simultaneously overfitting and underfitting, detail that it performs poorly on the training set (indicating high bias), and explain that its performance is even worse on the dev set (indicating high variance).