On a binary classification task, it is a priori true that you would likely not learn if you were right 50% or 100% of the time. This is not a function of any pa

On a binary classification task, it is a priori true that you would likely not learn if you were right 50% or 100% of the time. This is not a function of any particular learning algorithm. If you got 100% right, you already know everything that was being tested. If you got 50% right, you don't know if you are guessing or if you should be picking up on any features. So you would expect that the rate would not be close. 50.1% would be similar to 50% for most intents and purposes. Similarly 99.99%. So you might expect that the optimal learning rate would be close to 75%/25% in general. This would apply to humans too because it is a statement of the information you need to solve the problem, not a statement about the algo. This paper finds it to be 85%/15% for a particular algorithm. Perhaps humans learn similarly, perhaps not. However, you might expect the optimal examples to be somewhere in the 65-85% range for any particular algorithm.