Case context: You are training a large neural network to detect risky financial transactions. The model has unusually high error on the training set. You also have access to a large pool of unlabeled transaction records that could be labeled at a cost, and you can afford substantial computing time.

Question: According to modern deep learning practice, what should you do first to address the high training error, and what should you do if the model later still performs poorly on the development set?

Sample answer: First, treat the high training error as a sign of underfitting and increase the model’s capacity, while also revisiting the regularization strength so it is not too restrictive. After that, check the development set. If the development error remains high, the problem is now likely excess variance, so I would label more of the available transaction data and add it to training.

Key points:

• High training error indicates underfitting or bias.
• Increase model size and adjust regularization to reduce bias.
• High development error after that points to variance.
• More labeled training data is a good next step for variance.

Rubric: The response must identify high training error as a bias/underfitting problem and high development error as a variance problem. It must recommend increasing model capacity and tuning regularization first, then adding more labeled data if development performance is still poor.