Case context: You are reviewing the learning curve for a new prediction system. The desired error rate is 2%, but the training error has plateaued at 15%. Furthermore, the dev error is currently sitting at 28%.

Question: Based on this learning curve data, what should you diagnose about the system's bias and variance, and what is your broad recommendation for the algorithm?

Sample answer: The system has significant bias because the training error (15%) is much higher than the desired level of performance (2%). It also has significant variance because the dev error (28%) is much larger than the training error. The broad recommendation is to find ways to reduce both bias and variance in the algorithm.

Key points:

• Significant bias is present due to 15% training error vs 2% desired performance.
• Significant variance is present due to 28% dev error vs 15% training error.
• Recommendation is to reduce both bias and variance.

Rubric: The response must correctly identify high bias due to the high training error, identify high variance due to the gap between training and dev error, and recommend reducing both.