Question: Describe the ideal state of a classifier in terms of the two major sources of error, and explain what achieving this state indicates about its performance.

Sample answer: The ideal state of a classifier is to have both low bias and low variance. Since bias and variance are the two major sources of error, minimizing both means the classifier is making very few errors on the training set and generalizes well to the dev/test sets. Achieving this state indicates that the classifier is doing well and has reached great performance.

Key points:

• The classifier must have low bias.
• The classifier must have low variance.
• Achieving both indicates the classifier is doing well.
• This state represents great performance.

Rubric: A strong answer will explicitly mention low bias and low variance and connect these to the concept of the classifier doing well or achieving great performance.