Case context: You are building a speech recognition system. You estimate the optimal error rate (desired performance) to be 2%. After plotting the learning curve using all 10,000 hours of available audio data, you observe that your training error plateaus at 18%, and your dev error is 19%.

Question: Based on the standard textbook learning curve patterns, what specific problem does your speech recognition system have, and what do the respective gaps between the 2%, 18%, and 19% figures indicate?

Sample answer: The system suffers from high avoidable bias. The large gap of 16% between the desired performance (2%) and the training error (18%) indicates large avoidable bias, meaning the model is not doing well on the training set. Meanwhile, the small gap of 1% between the training error (18%) and the dev error (19%) indicates that the model has small variance.

Key points:

• The model has high avoidable bias.
• The gap between 2% and 18% indicates large avoidable bias.
• The gap between 18% and 19% indicates small variance.
• The model is performing poorly on the training set itself.

Rubric: The response must correctly diagnose the primary issue as high avoidable bias. It must explain that the large difference between desired performance and training error shows bias, and the small difference between training and dev error shows small variance.