Question: Explain why a good regularization method changes the usual concern about making a model larger, and identify the main tradeoff that still remains.

Sample answer: Without regularization, increasing model size can lower bias but also increase variance and make overfitting more likely. With a well-chosen regularization method, such as L2 regularization or dropout tuned on a development set, larger models often do not become more prone to overfitting in practice. In many cases, performance stays about the same or gets better. As a result, the usual size-versus-overfitting concern is greatly reduced. The main tradeoff that remains is computational cost: larger models require more memory, training time, and inference time, so resources become the key reason not to keep increasing size.

Key points:

• Larger models can reduce bias but raise overfitting risk when unregularized
• Good regularization weakens that overfitting concern
• L2 and dropout are examples of regularization that can be tuned on a dev set
• Performance often stays steady or improves as size grows under regularization
• Compute and runtime become the main limiting factors

Rubric: Full credit for explaining that regularization removes most of the overfitting penalty of larger models, noting that performance may stay the same or improve, and identifying computation as the remaining tradeoff. Partial credit for only part of this chain of reasoning.