2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Learning Latent Representations of Bank Customers With The Variational Autoencoder
Rogelio A Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the cre…
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Kjersti Aas, Martin Jullum, Anders Løland
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by…