Case context: A team finds that its learning algorithm is doing poorly on the training set. Error analysis also reveals a particular recurring category of errors. The team is considering a larger model, new features, weaker regularization, a revised architecture, or more training data.

Question: What should the team diagnose, which interventions are supported, and which proposed intervention should not be relied upon to solve the diagnosed problem?

Sample answer: The team should diagnose high avoidable bias. It can increase model size to improve training-set fit, create input features based on the recurring error category, reduce or eliminate regularization while recognizing that variance may increase, or modify the architecture to better suit the problem. It should not rely on more training data to reduce bias because additional data helps variance and usually has no significant effect on bias.

Key points:

• Poor training-set performance indicates high avoidable bias.
• A larger model is a supported intervention.
• Error analysis can motivate useful feature changes.
• Reducing regularization can increase variance.
• Architecture modification may affect both bias and variance.
• More data is not a helpful high-bias technique.

Rubric: The response should diagnose high avoidable bias, identify the four supported intervention categories, connect feature modification to error analysis, mention the variance tradeoff of reducing regularization, and reject more data as the main bias remedy.