Blar i Publikasjoner fra Cristin på forfatter "Jullum, Martin"
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Comparison of Contextual Importance and Utility with LIME and Shapley Values
Främling, Kary; Westberg, Marcus; Jullum, Martin; Madhikermi, Manik; Malhi, Avleen Kaur (Journal article; Peer reviewed, 2021)Different explainable AI (XAI) methods are based on different notions of ‘ground truth’. In order to trust explanations of AI systems, the ground truth has to provide fidelity towards the actual behaviour of the AI system. ... -
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, 2002)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 (Journal article, 2021) -
Pairwise local Fisher and naive Bayes: Improving two standard discriminants
Otneim, Håkon; Jullum, Martin; Tjøstheim, Dag Bjarne (Journal article; Peer reviewed, 2020)The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the naive Bayes, which is based on marginals only. In this paper we extend both discriminants ... -
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values
Sellereite, Nikolai; Jullum, Martin (Journal article; Peer reviewed, 2020)