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Data-driven advice for interpreting local and global model predictions in bioinformatics problems
For random forests, we find extremely high similarities and correlations of both local and global SHAP values and CFC scores, leading to very similar rankings and interpretations.
From unbiased MDI Feature Importance to Explainable AI for Trees
We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the the default variable-importance measure in random Forests, Gini importance.
Unbiased variable importance for random forests
The default variable-importance measure in random Forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting criterion.
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