Question: Describe a scenario where an algorithm achieves a training error close to the optimal error rate but has a much higher dev error. Explain what this implies about bias and variance, and discuss the potential for improvement in both areas.

Sample answer: In this scenario, the algorithm does well on the training set, meaning the training error is close to the optimal error rate. This indicates that the bias is small. However, because the dev error is much higher, the algorithm is not generalizing well to previously unseen data, which means the variance is large. Consequently, there is little room for bias improvement, but there is ample room for variance improvement. Adding more training data is a strategy that would probably help close the gap.

Key points:

• Training error near the optimal rate indicates small bias.
• High dev error relative to training error indicates large variance.
• There is little room for bias improvement.
• There is ample room for variance improvement.
• Adding training data can help close the generalization gap.

Rubric: A strong response will correctly identify low bias and high variance based on the described errors, and explain that improvement efforts should focus on variance (e.g., adding more data) rather than bias.