Case context: A product team has a neural network for fraud detection. They use dropout and L2 regularization, and both were tuned on the validation set. The team wants to lower bias by moving from a medium-sized network to a much larger one. In an earlier project, a larger unregularized model fit the training data too closely, so some team members are worried that the same thing will happen again. They have access to a modest amount of GPU time, but not unlimited capacity.

Question: Using the reasoning from the lesson, should the team mainly worry about overfitting, or about something else? What factor should drive the decision to increase model size?

Sample answer: If regularization has already been chosen and tuned well, then overfitting is usually not the main reason to avoid a bigger model. A larger network can often be trained without increasing validation error, and it may even improve performance. The more important question is whether the team can afford the extra computation. They should compare the added training time, memory use, and deployment cost against the expected benefit from the larger model.

Key points:

• Regularization is already in place and tuned on validation data
• A larger model is not automatically a worse choice because of overfitting
• Validation performance can stay the same or improve with more capacity
• Compute, memory, and deployment cost are the main trade-offs

Rubric: Full credit for stating that regularization reduces the overfitting concern and that computational cost is the key remaining issue. Partial credit for mentioning only one of those ideas.