Question: Explain, using the source's reasoning, why a well-designed regularization method changes the usual bias-variance tradeoff concern when increasing model size, and what tradeoff remains afterward.

Sample answer: Normally, increasing model size can reduce bias but raise variance and overfitting risk. The source explains that this overfitting problem usually only shows up when regularization isn't used. If you apply a well-tuned regularization method, such as L2 regularization or dropout with a dev-set-optimized regularization parameter, you can usually increase model size without meaningfully increasing overfitting; performance tends to stay the same or improve. This means the classic bias-variance tradeoff from model size is largely neutralized by regularization. What remains is a computational cost tradeoff: bigger models take more resources to train and run, and that becomes the main reason to hold back on model size.

Key points:

• Bigger models can reduce bias but risk raising variance/overfitting
• This overfitting risk usually arises only without regularization
• Well-tuned L2/dropout regularization allows safe model size increases
• Performance usually stays the same or improves with regularization
• Computational cost becomes the main remaining reason to avoid bigger models

Rubric: Full credit for explaining that regularization prevents the usual overfitting increase from bigger models, noting performance staying same/improving, and identifying computational cost as the remaining tradeoff. Partial credit for only one of these elements.