71 citations · 76 across the 4 of their papers we have counts for
4 papers · 1 filter
Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
Kristin Blesch, Niklas Koenen, Jan Kapar +4
This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance asse…
Data-driven advice for interpreting local and global model predictions in bioinformatics problems
Markus Loecher, Qi Wu
Tree-based algorithms such as random forests and gradient boosted trees continue to be among the most popular and powerful machine learning models used across multiple disciplines.…
Unbiased variable importance for random forests
Markus Loecher
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. While the altern…
From unbiased MDI Feature Importance to Explainable AI for Trees
Markus Loecher
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 measu…