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stat.ML2024
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Susanne Dandl, Kristin Blesch, Timo Freiesleben +4
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavio…
stat.ML2023
arfpy: A python package for density estimation and generative modeling with adversarial random forests
Kristin Blesch, Marvin N. Wright
This paper introduces , a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new dat…