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The Explanation Game: Explaining Machine Learning Models Using Shapley Values
Lecture Notes in Computer Science, 2020, Vol.LNCS-12279, p.17-38
[Peer Reviewed Journal]
Attribution ;DOI: 10.1007/978-3-030-57321-8_2
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Title:
The Explanation Game: Explaining Machine Learning Models Using Shapley Values
Author:
Merrick, Luke
;
Taly, Ankur
Subjects:
Computer Science
;
Humanities and Social Sciences
;
Library and information sciences
Is Part Of:
Lecture Notes in Computer Science, 2020, Vol.LNCS-12279, p.17-38
Description:
A number of techniques have been proposed to explain a machine learning model’s prediction by attributing it to the corresponding input features. Popular among these are techniques that apply the Shapley value method from cooperative game theory. While existing papers focus on the axiomatic motivation of Shapley values, and efficient techniques for computing them, they offer little justification for the game formulations used, and do not address the uncertainty implicit in their methods’ outputs. For instance, the popular SHAP algorithm’s formulation may give substantial attributions to features that play no role in the model. In this work, we illustrate how subtle differences in the underlying game formulations of existing methods can cause large differences in the attributions for a prediction. We then present a general game formulation that unifies existing methods, and enables straightforward confidence intervals on their attributions. Furthermore, it allows us to interpret the attributions as contrastive explanations of an input relative to a distribution of reference inputs. We tie this idea to classic research in cognitive psychology on contrastive explanations, and propose a conceptual framework for generating and interpreting explanations for ML models, called formulate, approximate, explain (FAE). We apply this framework to explain black-box models trained on two UCI datasets and a Lending Club dataset.
Related Titles:
Machine Learning and Knowledge Extraction
Publisher:
Springer International Publishing
Language:
English
Identifier:
DOI: 10.1007/978-3-030-57321-8_2
Source:
HAL SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)
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