An illustration of model agnostic explainability methods applied to environmental data.
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| Title: | An illustration of model agnostic explainability methods applied to environmental data. |
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| Authors: | 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. |
| Source: | Environmetrics [Environmetrics] 2023 Feb; Vol. 34 (1). Date of Electronic Publication: 2022 Oct 25. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 100968246 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1180-4009 (Print) Linking ISSN: 1099095X NLM ISO Abbreviation: Environmetrics Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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