When calculating feature importance, the leaf values directly correlate the value of the importance.
Consequently, positive leaf values -> positive feature importance
negative leaf values -> negative feature importance.
It follows that for binary classification, this is done such that the importance relates to the leaf values, which relate directly to the "probability of class 1".
So, the feature importance calculated is always for the importance as it relates to class 1.
The inverse is the importance as it relates to class 0.
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