scikit-learn-contrib / boruta_py

Python implementations of the Boruta all-relevant feature selection method.
BSD 3-Clause "New" or "Revised" License
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On the use of pruned trees #36

Open jay-reynolds opened 6 years ago

jay-reynolds commented 6 years ago

The README states: "We highly recommend using pruned trees with a depth between 3-7."

For my data, a depth of 3 rejects the fewest variables, increasing depth to 7 to a greater degree, and no pruning at all results in all variables being rejected.

I'm curious to know why this is, as I had expected that greater depth would afford greater sensitivity to subtle interactions between features.