Global Feature Importance node

Anyone who explain to me how global importance feature scores using RF surrogate model are computed ?

Thanks a lot

Hello @mdibiase2,

I would take a look at this component description:

Specifically the quote below,

A3) Surrogate Random Forest Model:
Random Forest is trained with optimized parameters “Tree Depth”, “Number of models” and “Minimum child node size”. Feature importance is calculated by counting how many times it has been selected for a split and at which rank (level) among all available features (candidates) in the trees of the random forest.

Hope this helps,


Thank you for responding!

Anyway, i already read that part of documentation, but i wondered if was possible to go more in depth about it.
For instance, what are ranks about ? The tree depth ?
And in particular, is there any example of math computing of the score ?