Why can’t adding more data by itself solve this learning-curve problem?
Question: Answer in one to three sentences using the relationship between training error and dev error.
Sample answer: If training error is already above the desired level, adding more examples will not make it lower; training error generally stays the same or increases as the training set grows. Since dev error is usually at least as high as training error, extra data alone is not enough to reach the target.
Key points:
- Training error does not decrease when more training data is added.
- Dev error is typically higher than training error.
- The current training error is already above the goal.
Rubric: Include both observations and explain why they show that more data alone will not solve the problem.
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Related
Why More Data May Not Fix a High-Training-Error Model
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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.