Question: In a concise response, explain how increasing model size, modifying input features, reducing regularization, and changing architecture can address high avoidable bias. Contrast them with adding data.

Sample answer: Increasing model size can reduce bias by allowing the algorithm to fit the training set better. Error analysis can motivate new input features that eliminate particular categories of errors. Reducing or eliminating L1, L2, or dropout regularization lowers avoidable bias but increases variance. A model architecture better suited to the problem can affect both bias and variance. In contrast, adding training data helps variance problems and usually has no significant effect on bias.

Key points:

• Larger models can fit the training set better and reduce bias.
• Feature changes should follow insights from error analysis.
• Reducing regularization lowers avoidable bias but increases variance.
• Architecture changes can affect both bias and variance.
• Additional training data usually has no significant effect on bias.

Rubric: A strong response accurately explains all four recommended techniques, states the relevant bias-variance effects, and clearly distinguishes additional training data as a variance-oriented intervention rather than a high-bias remedy.