Question: Describe the usual learning-curve pattern when a model has high avoidable bias. Include what happens to training error, validation error, and the target performance level at the largest training-set size.

Sample answer: When a model has high avoidable bias, the training error remains much worse than the target performance level even after using the largest training set available. That means the model is still underperforming on the examples it has seen, which is a sign of strong bias. In this pattern, the validation error stays close to the training error, so the gap between them is small. A small train-validation gap suggests the model does not have much variance, even though its bias is high.

Key points:

• Training error stays well above the target performance level
• A large gap between training error and target performance indicates high bias
• The model still performs poorly on the training data
• Training and validation error remain close together
• A small train-validation gap indicates low variance

Rubric: A full-credit response must explain that, at the largest training-set size, training error is still far from the target performance level and that the train-validation gap is small. The answer should connect the first gap to high bias and the second gap to low variance.