An illustration of model agnostic explainability methods applied to environmental data.

Wikle, Christopher K; Datta, Abhirup; Hari, Bhava Vyasa; Boone, Edward L; Sahoo, Indranil; Kavila, Indulekha; Castruccio, Stefano; Simmons, Susan J; Burr, Wesley S; Chang, Won

Wikle, Christopher K; Datta, Abhirup; Hari, Bhava Vyasa; Boone, Edward L; Sahoo, Indranil; Kavila, Indulekha; Castruccio, Stefano; Simmons, Susan J; Burr, Wesley S; Chang, Won.

Afiliación

Wikle CK; Department of Statistics, University of Missouri, Columbia, Missouri, USA.
Datta A; Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
Hari BV; Wipro Limited, Bengaluru, India.
Boone EL; Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, USA.
Sahoo I; Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, USA.
Kavila I; School of Pure and Applied Physics, Mahatma Gandhi University, Athirampuzha, Kerala, India.
Castruccio S; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
Simmons SJ; Institute for Advanced Analytics, North Carolina State University, Raleigh, North Carolina, USA.
Burr WS; Department of Mathematics, Trent University, Peterborough, Ontario, Canada.
Chang W; Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA.

Environmetrics ; 34(1)2023 Feb.

Article en En | MEDLINE | ID: mdl-37200542

Palabras clave

LIME; Shapley values; explainable AI; feature shuffling; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Environmetrics Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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