{"id":"https://openalex.org/W3172444956","doi":"https://doi.org/10.1109/access.2021.3084050","title":"The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen\u2019s Kappa and Brier Score in Binary Classification Assessment","display_name":"The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen\u2019s Kappa and Brier Score in Binary Classification Assessment","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3172444956","doi":"https://doi.org/10.1109/access.2021.3084050","mag":"3172444956"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3084050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3084050","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09440903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09440903.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011556172","display_name":"Davide Chicco","orcid":"https://orcid.org/0000-0001-9655-7142"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Davide Chicco","raw_affiliation_strings":["Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9655-7142","affiliations":[{"raw_affiliation_string":"Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026965468","display_name":"Matthijs J. Warrens","orcid":"https://orcid.org/0000-0002-7302-640X"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Matthijs J. Warrens","raw_affiliation_strings":["Groningen Institute for Educational Research, University of Groningen, Groningen, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-7302-640X","affiliations":[{"raw_affiliation_string":"Groningen Institute for Educational Research, University of Groningen, Groningen, The Netherlands","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090829168","display_name":"Giuseppe Jurman","orcid":"https://orcid.org/0000-0002-2705-5728"},"institutions":[{"id":"https://openalex.org/I2277624104","display_name":"Fondazione Bruno Kessler","ror":"https://ror.org/01j33xk10","country_code":"IT","type":"facility","lineage":["https://openalex.org/I2277624104"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giuseppe Jurman","raw_affiliation_strings":["Data Science for Health Unit, Fondazione Bruno Kessler, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-2705-5728","affiliations":[{"raw_affiliation_string":"Data Science for Health Unit, Fondazione Bruno Kessler, Trento, Italy","institution_ids":["https://openalex.org/I2277624104"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":24.1984,"has_fulltext":false,"cited_by_count":428,"citation_normalized_percentile":{"value":0.99817438,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"78368","last_page":"78381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13309","display_name":"Reliability and Agreement in Measurement","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13309","display_name":"Reliability and Agreement in Measurement","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13398","display_name":"Data Analysis with R","score":0.9578999876976013,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/brier-score","display_name":"Brier score","score":0.8920114040374756},{"id":"https://openalex.org/keywords/kappa","display_name":"Kappa","score":0.7045168280601501},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.6309285759925842},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5289055705070496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5188421010971069},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5188180804252625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4978334903717041},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.48786258697509766},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.45246174931526184},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.43045687675476074},{"id":"https://openalex.org/keywords/odds","display_name":"Odds","score":0.4139302968978882},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3853102922439575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34313899278640747},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3366815447807312},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.20935288071632385},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1359739899635315},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.10145267844200134}],"concepts":[{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.8920114040374756},{"id":"https://openalex.org/C2778724333","wikidata":"https://www.wikidata.org/wiki/Q14401","display_name":"Kappa","level":2,"score":0.7045168280601501},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.6309285759925842},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5289055705070496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5188421010971069},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5188180804252625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4978334903717041},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.48786258697509766},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.45246174931526184},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.43045687675476074},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.4139302968978882},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3853102922439575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34313899278640747},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3366815447807312},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.20935288071632385},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1359739899635315},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.10145267844200134},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/access.2021.3084050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3084050","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09440903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:pure.rug.nl:publications/631eebc1-7560-4645-82b9-54d9bf3d023b","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/631eebc1-7560-4645-82b9-54d9bf3d023b","pdf_url":"https://pure.rug.nl/ws/files/173877725/09440903.pdf","source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chicco, D, Warrens, M J & Jurman, G 2021, 'The Matthews correlation coefficient (MCC) is more informative than Cohen's kappa and Brier score in binary classification assessment', IEEE Access, vol. 9, 9440903, pp. 78368-78381. https://doi.org/10.1109/ACCESS.2021.3084050","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:boa.unimib.it:10281/430460","is_oa":true,"landing_page_url":"https://hdl.handle.net/10281/430460","pdf_url":null,"source":{"id":"https://openalex.org/S4306401259","display_name":"BOA (University of Milano-Bicocca)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66752286","host_organization_name":"University of Milano-Bicocca","host_organization_lineage":["https://openalex.org/I66752286"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:91ef0226db1248cf90447c75247d23bc","is_oa":true,"landing_page_url":"https://doaj.org/article/91ef0226db1248cf90447c75247d23bc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 78368-78381 (2021)","raw_type":"article"},{"id":"pmh:oai:iris.unitn.it:11572/343480","is_oa":true,"landing_page_url":"http://hdl.handle.net/11572/343480","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.rug.nl:openaire/631eebc1-7560-4645-82b9-54d9bf3d023b","is_oa":true,"landing_page_url":"https://hdl.handle.net/11370/631eebc1-7560-4645-82b9-54d9bf3d023b","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chicco, D, Warrens, M J & Jurman, G 2021, 'The Matthews correlation coefficient (MCC) is more informative than Cohen's kappa and Brier score in binary classification assessment', IEEE Access, vol. 9, 9440903, pp. 78368-78381. https://doi.org/10.1109/ACCESS.2021.3084050","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3084050","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3084050","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09440903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":104,"referenced_works":["https://openalex.org/W39749807","https://openalex.org/W96748260","https://openalex.org/W165705326","https://openalex.org/W1596163296","https://openalex.org/W1909740415","https://openalex.org/W1937501893","https://openalex.org/W1971654961","https://openalex.org/W1976658129","https://openalex.org/W1976931151","https://openalex.org/W1978860388","https://openalex.org/W1982264966","https://openalex.org/W1983393113","https://openalex.org/W1983897914","https://openalex.org/W1984319517","https://openalex.org/W1988301066","https://openalex.org/W1991555897","https://openalex.org/W2004655916","https://openalex.org/W2006255103","https://openalex.org/W2016136817","https://openalex.org/W2021560851","https://openalex.org/W2023217251","https://openalex.org/W2026096417","https://openalex.org/W2030644393","https://openalex.org/W2030888262","https://openalex.org/W2034901280","https://openalex.org/W2035306675","https://openalex.org/W2037789405","https://openalex.org/W2042436876","https://openalex.org/W2043644439","https://openalex.org/W2046280714","https://openalex.org/W2048839183","https://openalex.org/W2052497509","https://openalex.org/W2052587030","https://openalex.org/W2053154970","https://openalex.org/W2056616772","https://openalex.org/W2059185913","https://openalex.org/W2064397275","https://openalex.org/W2064848794","https://openalex.org/W2070285512","https://openalex.org/W2071035486","https://openalex.org/W2073241381","https://openalex.org/W2076061140","https://openalex.org/W2076898331","https://openalex.org/W2082471284","https://openalex.org/W2084396669","https://openalex.org/W2085996979","https://openalex.org/W2087656216","https://openalex.org/W2089861931","https://openalex.org/W2097375928","https://openalex.org/W2099174412","https://openalex.org/W2104133108","https://openalex.org/W2104896032","https://openalex.org/W2105941276","https://openalex.org/W2107432340","https://openalex.org/W2107980537","https://openalex.org/W2109553965","https://openalex.org/W2110101012","https://openalex.org/W2117110318","https://openalex.org/W2121551382","https://openalex.org/W2123679438","https://openalex.org/W2133012565","https://openalex.org/W2135198734","https://openalex.org/W2151161781","https://openalex.org/W2153942520","https://openalex.org/W2153948059","https://openalex.org/W2155822698","https://openalex.org/W2158676734","https://openalex.org/W2160554939","https://openalex.org/W2165570175","https://openalex.org/W2170021941","https://openalex.org/W2177380131","https://openalex.org/W2184066413","https://openalex.org/W2191825358","https://openalex.org/W2324792924","https://openalex.org/W2333035970","https://openalex.org/W2341477492","https://openalex.org/W2500279295","https://openalex.org/W2517596076","https://openalex.org/W2531301769","https://openalex.org/W2592955804","https://openalex.org/W2620760558","https://openalex.org/W2771169143","https://openalex.org/W2773944606","https://openalex.org/W2775986596","https://openalex.org/W2797145544","https://openalex.org/W2897204591","https://openalex.org/W2975256032","https://openalex.org/W2999309192","https://openalex.org/W3011821385","https://openalex.org/W3028751448","https://openalex.org/W3030391949","https://openalex.org/W3036465927","https://openalex.org/W3047479470","https://openalex.org/W3126232929","https://openalex.org/W3139397034","https://openalex.org/W4232120991","https://openalex.org/W4243333538","https://openalex.org/W4255783720","https://openalex.org/W4287691981","https://openalex.org/W4313429089","https://openalex.org/W6635704139","https://openalex.org/W6780162762","https://openalex.org/W6781538126","https://openalex.org/W6944994628"],"related_works":["https://openalex.org/W1967594194","https://openalex.org/W1444168786","https://openalex.org/W2294084971","https://openalex.org/W2148964467","https://openalex.org/W2281321759","https://openalex.org/W2474048269","https://openalex.org/W2792864698","https://openalex.org/W1966976587","https://openalex.org/W2292870489","https://openalex.org/W4294214983"],"abstract_inverted_index":{"Even":[0],"if":[1,100],"measuring":[2],"the":[3,33,36,48,51,56,63,101,104,110,113,150,166,170,207,222],"outcome":[4],"of":[5,50,103,112,176],"binary":[6,227],"classifications":[7],"is":[8,211],"a":[9,96,142,153,199],"pivotal":[10],"task":[11],"in":[12,93,146,160],"machine":[13],"learning":[14],"and":[15,39,88,109,119,149,169,174,202,221],"statistics,":[16],"no":[17],"consensus":[18],"has":[19],"been":[20],"reached":[21],"yet":[22],"about":[23],"which":[24,158,191],"statistical":[25],"rate":[26],"to":[27,29,55,70,193,214,225],"employ":[28],"this":[30,130],"end.":[31],"In":[32,129],"last":[34],"century,":[35],"computer":[37],"science":[38],"statistics":[40],"communities":[41],"have":[42,71],"introduced":[43],"several":[44,72],"scores":[45,187],"summing":[46],"up":[47],"correctness":[49],"predictions":[52],"with":[53,135],"respect":[54],"ground":[57],"truth":[58],"values.":[59],"Among":[60],"these":[61,177,186],"scores,":[62],"Matthews":[64],"correlation":[65],"coefficient":[66],"(MCC)":[67],"was":[68],"shown":[69],"advantages":[73],"over":[74],"confusion":[75],"entropy,":[76],"accuracy,":[77,84],"F":[78],"<sub":[79],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[80],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[81],"score,":[82,152],"balanced":[83],"bookmaker":[85],"informedness,":[86],"markedness,":[87],"diagnostic":[89],"odds":[90],"ratio:":[91],"MCC,":[92],"fact,":[94],"produces":[95],"high":[97],"score":[98,224],"only":[99],"majority":[102,111],"predicted":[105],"negative":[106],"data":[107,115],"instances":[108,116],"positive":[114],"are":[117],"correct,":[118],"therefore":[120],"it":[121,210],"results":[122],"being":[123],"very":[124],"trustworthy":[125],"on":[126],"imbalanced":[127],"datasets.":[128],"study,":[131],"we":[132,180],"compare":[133],"MCC":[134,173,197,216],"two":[136,178],"other":[137],"popular":[138],"scores:":[139],"Cohen's":[140,219],"Kappa,":[141],"metric":[143],"that":[144,218],"originated":[145],"social":[147],"sciences,":[148],"Brier":[151,223],"strictly":[154],"proper":[155],"scoring":[156],"function":[157],"emerged":[159],"weather":[161],"forecasting":[162],"studies.":[163],"After":[164],"explaining":[165],"mathematical":[167],"properties":[168],"relationships":[171],"between":[172],"each":[175],"rates,":[179],"report":[181],"some":[182],"use":[183,215],"cases":[184],"where":[185,196],"generate":[188],"different":[189],"values,":[190],"lead":[192],"discordant":[194],"outcomes,":[195],"provides":[198],"more":[200,212],"truthful":[201],"informative":[203],"result.":[204],"We":[205],"highlight":[206],"reasons":[208],"why":[209],"advisable":[213],"rather":[217],"Kappa":[220],"evaluate":[226],"classifications.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":31},{"year":2025,"cited_by_count":119},{"year":2024,"cited_by_count":105},{"year":2023,"cited_by_count":104},{"year":2022,"cited_by_count":50},{"year":2021,"cited_by_count":19}],"updated_date":"2026-06-18T10:00:31.954636","created_date":"2025-10-10T00:00:00"}
