Case context: You are analyzing a machine learning model where bias/variance analysis indicates the primary source of error is high avoidable bias. Your team is proposing two categories of changes: Group A changes focus on improving performance on the training set, while Group B changes focus on helping the model generalize better from the training set to the dev/test sets.

Question: Based on the principles in Machine Learning Yearning, which group of changes (Group A or Group B) should you prioritize, and why?

Sample answer: You should prioritize Group A changes. High avoidable bias means the model's performance on the training set needs improvement. According to Machine Learning Yearning, changes that address bias improve performance on the training set, whereas changes that address variance help the model generalize from the training set to the dev/test sets. Therefore, prioritizing Group A changes directly targets the primary source of error.

Key points:

• Group A changes address avoidable bias by improving training set performance.
• Group B changes address variance by helping the model generalize from training to dev/test sets.
• Prioritizing techniques should align with the current primary problem (high avoidable bias).

Rubric: The response should: 1. State that Group A changes should be prioritized. 2. Explain that Group A changes address bias and improve performance on the training set. 3. Explain that Group B changes address variance by helping the model generalize from training to dev/test sets, which is not the primary issue here.