Browsing NR vitenarkiv by Author "Redelmeier, Annabelle Alice"
Now showing items 1-5 of 5
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Efficient and simple prediction explanations with groupShapley: A practical perspective
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (Peer reviewed; Journal article, 2021) -
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees
Redelmeier, Annabelle Alice; Jullum, Martin; Aas, Kjersti (Chapter, 2022)It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from ... -
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (NR-notat;, Research report, 2021) -
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (Journal article, 2021) -
MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data
Redelmeier, Annabelle Alice; Jullum, Martin; Aas, Kjersti; Løland, Anders (Journal article; Peer reviewed, 2024)We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by ...