# Does the number of rows really matter beyond a point?

While working with any machine learning algorithm, does the number of rows really matter beyond a certain point?

I have kept some algorithms(decision tree in this instance) running for days, and the accuracy I get is similar to those I get immediately for 5000 rows. I'm kind of feeling that running it on the entire data set is more like a 'check' to verify the results you get with 5000 rows.

Is there any study on how many rows are actually needed and how much results vary when increasing the number of rows?

Answer here will probably be application-specific, data-set specific. In any given situation you can try yourself, select $m$ (say, 5000) rows at random, do the same again, again, ... and compare results.