2 citations · 2 across the 3 of their papers we have counts for
3 papers
mlr3summary: Concise and interpretable summaries for machine learning models
Susanne Dandl, Marc Becker, Bernd Bischl +2
This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models…
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…
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Julia Herbinger, Susanne Dandl, Fiona K. Ewald +2
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation. This paper focuses on using mode…