Question: What combination of error characteristics would you hope to see in a classifier, and what does that tell you about the model overall?

Sample answer: A desirable classifier has both low bias and low variance. Low bias means it captures the main pattern in the data instead of consistently missing it, and low variance means its predictions stay stable when the input data changes slightly. When both are low, the model usually performs well on the examples it was trained on and also on new examples, which is a sign of strong overall performance.

Key points:

• Low bias is part of the ideal outcome.
• Low variance is part of the ideal outcome.
• Having both means the model is likely performing well.
• This combination indicates strong overall performance.

Rubric: A good response should state that the classifier should have low bias and low variance, and should explain that this reflects strong performance and good generalization.