Case context: You are training an image recognition model. You determine the optimal error rate based on human-level performance is 2%. However, your model achieves a 1% error rate on the training set, leading to a negative avoidable bias calculation. Your team suggests running a larger, more complex model to further reduce bias.

Question: What diagnosis should you make about the model's current state based on the negative avoidable bias, and how should you respond to your team's suggestion?

Sample answer: The model is currently overfitting because achieving an error rate below the optimal error rate on the training set means it has over-memorized the data. I should reject the team's suggestion to use a more complex model, as that focuses on bias reduction and would likely exacerbate the overfitting. Instead, we must focus on variance reduction methods.

Key points:

• The model is overfitting the training set.
• It has over-memorized the training data.
• Reject the team's suggestion for bias reduction.
• Focus on variance reduction methods.

Rubric: The response must diagnose the model as overfitting and prescribe variance reduction instead of the team's suggested bias reduction.