Statistics - Train and test data split
Those rule of thumbs for ratio make no sense. The use case for a test set is to measure your performance in a real scenario on unseen data. How much you need for an accurate measure depends on the level of accuracy you need, the amount of variance you expect, not so much on the size of the original dataset. Something to be aware of is that you do decrease the size of your training set, which is a cost of testing. But when you have 10 million rows of data it makes no sense to validate on 3 million, when 10,000 could be more than enough. Outside of the cost of losing training data you should look at absolute numbers in your test set.