I am having trouble with the intuition for running several RF models.
I have a few features (between 3 and 10) which should be correlated, since they measure things that are usually related.
I want to tune the
maximum depth of the tree, and the
min samples at each leaf -both of which are used as stopping criteria.
Since the data is correlated, my best intuition is that I would want to make each decision tree as deep as possible, and err on the side of a few min samples at each leaf (let's say 10, given that there are only about 1000 data points). My justification for this is that there is little concern for over-fitting since the data is correlated.
Is this intuition correct? And if not, what is a good way to optimize these two parameters?