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Decision Tree Feature Importance #200

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cmougan opened this issue Jul 25, 2020 · 0 comments

cmougan commented Jul 25, 2020

I believe that in section 4.4.1 (https://christophm.github.io/interpretable-ml-book/tree.html) you are just defining one type of feature importance, there are more types and maybe it can be interesting for readers to know about them.

In the xgboost API they define the following:
Get feature importance of each feature. Importance type can be defined as:

‘weight’: the number of times a feature is used to split the data across all trees.
‘gain’: the average gain across all splits the feature is used in.
‘cover’: the average coverage across all splits the feature is used in.
‘total_gain’: the total gain across all splits the feature is used in.
‘total_cover’: the total coverage across all splits the feature is used in.

This can be considered as a more thorough extension.

PD. The book is really interesting thanks :)

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