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• If you have worked on a important application for a long time, then you might have
intuition about how much more progress you can reasonably make in the next
quarter/year.

Add the desired level of performance to your learning curve:

You can visually extrapolate the red “dev error” curve to guess how much closer you could

get to the desired level of performance by adding more data. In the example above, it looks
plausible that doubling the training set size might allow you to reach the desired
performance.

But if the dev error curve has “plateaued” (i.e. flattened out), then you can immediately tell
that adding more data won’t get you to your goal:

Looking at the learning curve might therefore help you avoid spending months collecting
twice as much training data, only to realize it does not help.

Page 57 Machine Learning Yearning-Draft Andrew Ng