Case context: You are building a crop disease classifier. The model has a 3% error rate on the training set and a 10% error rate on the development set. An experienced agronomist has about a 1% error rate.

Question: Using these error numbers, identify the main issue with the model. What machine learning terms describe this situation, and what do the 2% and 7% figures represent?

Sample answer: The main issue is high variance, also described as overfitting. The 2% gap between the training error (3%) and the human-level error (1%) is the estimated bias. The 7% gap between the development error (10%) and the training error (3%) is the estimated variance. Because the variance is much larger than the bias gap, the model is not generalizing well to new data.

Key points:

• Diagnose the problem as overfitting or high variance.
• Identify the 2% training-to-human gap as the estimated bias.
• Identify the 7% development-to-training gap as the estimated variance.
• Explain that the larger gap on the development set shows poor generalization.

Rubric: A correct response must diagnose overfitting or high variance. It should correctly label the 2% gap as estimated bias and the 7% gap as estimated variance.