Why is recommended depth of Boruta random forest between 3 and 7? On the BorutaPy GitHub page, the authors state: "We highly recommend using pruned trees with a depth between 3-7" (https://github.com/scikit-learn-contrib/boruta_py). What justification is there for using a depth in this range? I've seen this recommendation reiterated many times by others citing the BorutaPy authors without explanation. If I have many features and suspect that there are highly complex patterns in my dataset, would you recommend deviating from this suggestion and using a larger depth?

It's a heuristic algorithm, based on a clever idea and backed up by some empirical results, but as such, it does not give you any guarantees of correctness. The recommendation most likely is based on empirical observations. So yes, it can be the case that different hyperparameters may behave differently for different cases.
