2 citations · 5 across the 4 of their papers we have counts for
4 papers
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum, Lars Henry Berge Olsen, Jon Lachmann +1
This paper introduces the shapr R package, a versatile tool for generating Shapley value-based prediction explanations for machine learning and statistical regression models. Moreo…
MCCE: Monte Carlo sampling of realistic counterfactual explanations
Annabelle Redelmeier, Martin Jullum, Kjersti Aas +1
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, a…
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Martin Jullum, Annabelle Redelmeier, Kjersti Aas
Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from complex machine learning models. The popular…
Explaining predictive models with mixed features using Shapley values and conditional inference trees
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
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…