{"id":"https://openalex.org/W7225450389","doi":"https://doi.org/10.5281/zenodo.24025259","title":"Platt Scaling Versus Isotonic Calibration: Brier Scores on Five UCI Classifiers and Why Recalibration Can Raise the Score","display_name":"Platt Scaling Versus Isotonic Calibration: Brier Scores on Five UCI Classifiers and Why Recalibration Can Raise the Score","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225450389","doi":"https://doi.org/10.5281/zenodo.24025259"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24025259","doi":"https://doi.org/10.5281/zenodo.24025259","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24025259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24025259","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159002730","display_name":"Manoj Kumar Aguru","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405266258","display_name":"Chaitanya","ror":"https://ror.org/01m1bpg27","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405266258"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aguru, Manoj Kumar","raw_affiliation_strings":["Former Officer, Chaitanya Godavari Grameena Bank (CGGB)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Former Officer, Chaitanya Godavari Grameena Bank (CGGB)","institution_ids":["https://openalex.org/I4405266258"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5159002730"],"corresponding_institution_ids":["https://openalex.org/I4405266258"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.5770999789237976,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.5770999789237976,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.1923999935388565,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.028200000524520874,"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/platt-scaling","display_name":"Platt scaling","score":0.9599999785423279},{"id":"https://openalex.org/keywords/isotonic-regression","display_name":"isotonic regression","score":0.9269999861717224},{"id":"https://openalex.org/keywords/brier-score","display_name":"Brier score","score":0.8949999809265137},{"id":"https://openalex.org/keywords/binary-classification","display_name":"binary classification","score":0.6800000071525574},{"id":"https://openalex.org/keywords/banknote-recognition","display_name":"banknote recognition","score":0.48500001430511475},{"id":"https://openalex.org/keywords/ionosphere","display_name":"ionosphere","score":0.9470000267028809},{"id":"https://openalex.org/keywords/sonar","display_name":"sonar","score":0.9929999709129333}],"concepts":[{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.9391999840736389},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.626800000667572},{"id":"https://openalex.org/C17418463","wikidata":"https://www.wikidata.org/wiki/Q3455874","display_name":"Isotonic regression","level":3,"score":0.6254000067710876},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6150000095367432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48840001225471497},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C77056095","wikidata":"https://www.wikidata.org/wiki/Q543183","display_name":"Isotonic","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33250001072883606},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.2736999988555908},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C68443243","wikidata":"https://www.wikidata.org/wiki/Q2786686","display_name":"Quartile","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.25859999656677246}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24025259","doi":"https://doi.org/10.5281/zenodo.24025259","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24025259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24025260","doi":"https://doi.org/10.5281/zenodo.24025260","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24025260","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24025259","doi":"https://doi.org/10.5281/zenodo.24025259","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24025259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3084965078","https://openalex.org/W2978456972","https://openalex.org/W7127045350","https://openalex.org/W7201890597","https://openalex.org/W7223266216","https://openalex.org/W2769911997","https://openalex.org/W2725875209","https://openalex.org/W7099058977","https://openalex.org/W3161561506","https://openalex.org/W2744092719"],"abstract_inverted_index":{"Probabilistic":[0],"classifiers":[1],"are":[2,24,48],"often":[3],"recalibrated":[4],"by":[5,71],"Platt":[6,81],"scaling":[7],"(logistic":[8],"regression":[9,22],"on":[10,32,84],"scores)":[11],"or":[12,123],"isotonic":[13,68,83],"regression.":[14],"On":[15],"five":[16,45],"UCI":[17],"binary":[18],"data":[19,46],"sets,":[20],"logistic":[21],"probabilities":[23],"evaluated":[25],"out":[26],"of":[27,36,100],"fold,":[28],"with":[29,93],"calibrators":[30],"fit":[31],"a":[33],"held-out":[34],"slice":[35,127],"each":[37],"training":[38],"fold.":[39],"Mean":[40],"Brier":[41,61],"scores":[42],"across":[43],"the":[44,58,98,116,125],"sets":[47],"0.0746":[49],"(raw),":[50],"0.0779":[51],"(Platt)":[52],"and":[53,64,73,78],"0.0817":[54],"(isotonic):":[55],"recalibration":[56],"raises":[57],"cross-data-set":[59],"average":[60,85],"score.":[62],"Banknote":[63],"spambase":[65],"improve":[66],"under":[67,106],"(Brier":[69],"falls":[70],"0.0034":[72],"0.0020),":[74],"while":[75],"wdbc,":[76],"ionosphere":[77],"sonar":[79],"worsen.":[80],"beats":[82],"(0.0779":[86],"against":[87],"0.0817).":[88],"The":[89],"Murphy":[90],"reliability-resolution-uncertainty":[91],"split":[92],"ten":[94],"equal-frequency":[95],"bins":[96],"tracks":[97],"ranking":[99],"methods":[101],"but":[102],"is":[103,110,119,128],"only":[104],"approximate":[105],"coarse":[107],"binning.":[108],"Recalibration":[109],"not":[111],"guaranteed":[112],"to":[113],"help":[114],"when":[115,124],"base":[117],"model":[118],"already":[120],"well":[121],"calibrated":[122],"calibration":[126],"small.":[129]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
