My questions are about Random Forests. The concept of this beautiful classifier is clear to me, but still there are a lot of practical usage questions. Unfortunately, I failed to find any practical guide to RF (I've been searching for something like "A Practical Guide for Training Restricted Boltzman Machines" by Geoffrey Hinton, but for Random Forests!
How can one tune RF in practice?
Is it true that bigger number of trees is always better? Is there a reasonable limit (except comp. capacity of course) on increasing number of trees and how to estimate it for given dataset?
What about depth of the trees? How to choose the reasonable one? Is there a sense in experimenting with trees of different length in one forest and what is the guidance for that?
Are there any other parameters worth looking at when training RF? Algos for building individual trees may be?
When they say RF are resistant to overfitting, how true is that?
I'll appreciate any answers and/or links to guides or articles that I might have missed while my search.