"Variable Importance - ranks variables by their ability to minimize error when split upon, averaged across all trees"

[burlachenkok]

First page. "Variable Importance - ranks variables by their ability to
minimize error when split upon, averaged across all trees".

Estimating the importance of a variable is quite an abstract and broad thing.

You can do this in a large number of ways if you require theory (and generally infinite - if you don’t require theory).

Things I know about evaluation variables are here https://sites.google.com/site/burlachenkok/some-ways-to-intepretate-black-box-models

The text from this cheat sheet in the context of decision trees was suggested by J.Friedman's friend - L. Breiman from 1983, J. Friedman has a theorem from 1999 that this is in some sense a good thing.

I suggest to add extra text, because one more time think that Variable importance can be done only in one way - is deeply wrong.

And thanks for your work