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A machine learning research template for binary classification problems and shapley values integration
dc.contributor.author | Smith, Matthew |
dc.contributor.author | Alvarez, Francisco |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2021-05-19T12:36:55Z |
dc.date.available | 2021-05-19T12:36:55Z |
dc.date.issued | 2021-05 |
dc.identifier.citation | Smith, M.; Alvarez, F. A machine learning research template for binary classification problems and shapley values integration. "Software Impacts", Maig 2021, vol. 8, 100074. |
dc.identifier.issn | 2665-9638 |
dc.identifier.uri | http://hdl.handle.net/2117/345914 |
dc.description.abstract | This paper documents published code which can help facilitate researchers with binary classification problems and interpret the results from a number of Machine Learning models. The original paper was published in Expert Systems with Applications and this paper documents the code and work-flow with a special interest being paid to Shapley values as a means to interpret Machine Learning predictions. The Machine Learning models used are, Naive Bayes, Logistic Regression, Random Forest, adaBoost, Classification Tree, Light GBM and XGBoost. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights | Attribution 3.0 Spain |
dc.rights | Attribution 4.0 International (CC BY 4.0) |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Enginyeria del software |
dc.subject.lcsh | COVID-19 (Disease) |
dc.subject.lcsh | Machine learning |
dc.subject.other | Machine Learning |
dc.subject.other | Binary classification |
dc.subject.other | COVID19 |
dc.subject.other | Shapley values |
dc.title | A machine learning research template for binary classification problems and shapley values integration |
dc.type | Article |
dc.subject.lemac | COVID-19 (Malaltia) |
dc.subject.lemac | Codi font (Informàtica) |
dc.identifier.doi | 10.1016/j.simpa.2021.100074 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2665963821000221 |
dc.rights.access | Open Access |
dc.description.version | Postprint (published version) |
local.citation.other | 100074 |
local.citation.publicationName | Software Impacts |
local.citation.volume | 8 |
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