About: Generalized SHAP: Generating multiple types of explanations in machine learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Generalized SHAP: Generating multiple types of explanations in machine learning
Creator	Bowen, Dillon Ungar, Lyle
source	ArXiv
abstract	Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley Additive Explanations (SHAP). Our new method - Generalized Shapley Additive Explanations (G-SHAP) - produces many additional types of explanations, including: 1) General classification explanations; Why is this sample more likely to belong to one class rather than another? 2) Intergroup differences; Why do our model's predictions differ between groups of observations? 3) Model failure; Why does our model perform poorly on a given sample? We formally define these types of explanations and illustrate their practical use on real data.
has issue date	2020-06-12(xsd:dateTime)
has license	arxiv
sha1sum (hex)	a952641049545b95e358ab6fc9d98a7bd6fd4fce
resource representing a document's title	Generalized SHAP: Generating multiple types of explanations in machine learning
resource representing a document's body	covid:a952641049545b95e358ab6fc9d98a7bd6fd4fce#body_text
is schema:about of	named entity 'mathematically' named entity 'Our' named entity 'Many' named entity 'ADDITIONAL' named entity 'PERFORM' »more»

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