Question: In a short response, explain how increasing model capacity, adding more informative inputs, lowering regularization, and choosing a better-fitting architecture can reduce high avoidable bias. Then contrast these actions with collecting additional training examples.

Sample answer: A larger model can reduce bias by giving the learner enough capacity to fit the training data more closely. Adding or redesigning inputs can remove repeated error patterns when error analysis shows the model is missing important information. Lowering L1, L2, or dropout regularization can also reduce avoidable bias, although it may increase variance. Choosing an architecture that matches the problem better can improve performance by changing the bias-variance tradeoff. By contrast, collecting more training data usually helps with variance and does not usually fix high bias on its own.

Key points:

• More model capacity can lower bias by improving fit to the training set.
• Input changes should be driven by observed error patterns.
• Reducing regularization lowers bias but can raise variance.
• Architecture choice can influence both bias and variance.
• More data is mainly a variance remedy, not a direct solution to high bias.

Rubric: A strong response explains each of the four tactics, states how each affects bias and variance, and makes clear that more training data is generally not the main fix for high bias.