
estimators and you have a separate validation set and if the
validation set accuracy doesn't improve or if it decreases
for number of iterations, say five, then you just stop the
learning. This way, you get results faster, because you
don't keep learning but also you possibly get a better model
because you know the overfitting.

The only downside stopping learning is that you have fewer
data to train your model because you need to the validation
set for early stopping. You’ll still need a separate test
s