Why More Data May Not Fix a High-Training-Error Model

Adding more training examples by itself can guarantee that dev error will reach a target level even when training error is still above that target.

When the training set gets larger, the training error can only stay the same or _____.

Match each learning-curve element to its role in diagnosing when extra data will not solve the problem.

Order the logic that shows more data by itself will not achieve the target performance.

Why more data alone cannot solve high training error

Decide whether more data alone can close a large training gap.

Why can’t adding more data by itself solve this learning-curve problem?

Which learning-curve pattern most clearly shows that collecting more data by itself will not solve the problem?

If training error does not improve as more labeled examples are added, development error can still be expected to drop below a lower target level from extra data alone.