Question: Explain what the training and development errors typically look like when a model has both high bias and high variance, and state the broad direction for improving the model.

Sample answer: When a model has high bias, the training error stays high, so performance on the training set is still far from what we want. When it also has high variance, the development error is much worse than the training error, showing that the model does not generalize well. Seeing both patterns at once means the model needs changes that lower both underfitting and overfitting, not just one or the other.

Key points:

• Training error remains much worse than desired, which signals high bias
• Development error is much higher than training error, which signals high variance
• The model must be improved in ways that address both problems

Rubric: A strong response must describe the high training error, the large gap between development and training error, and the need to reduce both bias and variance.