Case context: You are building a fraud screening system for online payments. Your target error rate for a strong system is 3%. After training on 8,000,000 transactions, you find that the training error levels off at 17%, and the dev error is 18%.

Question: Using the standard learning-curve pattern, what problem is this model showing, and what do the gaps between 3%, 17%, and 18% mean?

Sample answer: The model has high avoidable bias. The 14% gap between the target error rate (3%) and the training error (17%) shows that the model is underperforming on the training set, which is a sign of large bias. The 1% gap between the training error (17%) and the dev error (18%) shows that variance is small.

Key points:

• The main issue is high avoidable bias.
• The gap between 3% and 17% indicates large avoidable bias.
• The gap between 17% and 18% indicates small variance.
• The model is not fitting the training data well.

Rubric: The response must identify high avoidable bias as the primary issue. It must explain that the large gap between target performance and training error reflects bias, and that the small gap between training and dev error reflects low variance.