{"id":"https://openalex.org/W7162397470","doi":"https://doi.org/10.48550/arxiv.2605.25773","title":"Efficient Benchmarking Is Just Feature Selection and Multiple Regression","display_name":"Efficient Benchmarking Is Just Feature Selection and Multiple Regression","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162397470","doi":"https://doi.org/10.48550/arxiv.2605.25773"},"language":"en","primary_location":{"id":"pmh:oai:research-information.bris.ac.uk:publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","pdf_url":"https://research-information.bris.ac.uk/ws/files/489675320/2605.25773v2.pdf","source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bowyer, S, Locatelli, A & Cao, K 2026 'Efficient Benchmarking Is Just Feature Selection and Multiple Regression' arXiv.org. < https://arxiv.org/pdf/2605.25773 >","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research-information.bris.ac.uk/ws/files/489675320/2605.25773v2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038277295","display_name":"Sam Bowyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bowyer, Sam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003520377","display_name":"Acyr Locatelli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Locatelli, Acyr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029820246","display_name":"Kris Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Kris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10028","display_name":"Topic Modeling","score":0.26489999890327454,"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/T10028","display_name":"Topic Modeling","score":0.26489999890327454,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.24560000002384186,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.08139999955892563,"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/benchmarking","display_name":"Benchmarking","score":0.7415000200271606},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5559999942779541},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.538100004196167},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5098000168800354},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4819999933242798},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.38670000433921814},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.37439998984336853},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3671000003814697},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3587999939918518}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7415000200271606},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140999794006348},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5972999930381775},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.538100004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5338000059127808},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4950999915599823},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3402999937534332},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2630999982357025},{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:research-information.bris.ac.uk:publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","pdf_url":"https://research-information.bris.ac.uk/ws/files/489675320/2605.25773v2.pdf","source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bowyer, S, Locatelli, A & Cao, K 2026 'Efficient Benchmarking Is Just Feature Selection and Multiple Regression' arXiv.org. < https://arxiv.org/pdf/2605.25773 >","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2605.25773","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25773","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:research-information.bris.ac.uk:publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/f8a7b4e4-af0e-4950-abdc-f282b77c6919","pdf_url":"https://research-information.bris.ac.uk/ws/files/489675320/2605.25773v2.pdf","source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bowyer, S, Locatelli, A & Cao, K 2026 'Efficient Benchmarking Is Just Feature Selection and Multiple Regression' arXiv.org. < https://arxiv.org/pdf/2605.25773 >","raw_type":"info:eu-repo/semantics/preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7162397470.pdf","grobid_xml":"https://content.openalex.org/works/W7162397470.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"benchmarking":[1,43],"techniques":[2],"aim":[3],"to":[4,156],"lower":[5],"the":[6,56,158],"computational":[7],"cost":[8],"of":[9,21,32,125],"evaluating":[10],"LLMs":[11],"by":[12,49,78],"predicting":[13],"full":[14],"benchmark":[15],"scores":[16,114],"using":[17,51,60,127],"only":[18],"a":[19,22,123],"subset":[20],"benchmark's":[23],"questions.":[24],"By":[25],"reframing":[26],"this":[27],"problem":[28],"as":[29],"an":[30,61],"instance":[31],"multiple":[33],"regression":[34,54],"with":[35],"feature":[36],"selection,":[37],"we":[38,71],"find":[39],"that":[40,82],"existing":[41],"efficient":[42],"methods":[44,77,141],"can":[45,72,171],"be":[46,84,172],"greatly":[47],"improved":[48],"simply":[50],"kernel":[52],"ridge":[53],"at":[55,174],"prediction":[57,99],"stage.":[58],"Additionally,":[59],"information-theoretic":[62],"feature-selection":[63],"algorithm":[64],"called":[65],"minimum":[66],"redundancy":[67],"maximum":[68],"relevance":[69],"(mRMR),":[70],"further":[73],"improve":[74],"upon":[75],"these":[76,94],"selecting":[79],"question":[80],"subsets":[81],"will":[83],"maximally":[85],"useful":[86],"for":[87],"prediction.":[88],"Except":[89],"in":[90],"very":[91],"data-poor":[92],"settings,":[93],"approaches":[95],"consistently":[96],"achieve":[97],"smaller":[98],"errors":[100],"(in":[101,115],"both":[102,116,128],"MAE":[103],"and":[104,106,112,119,130,152],"RMSE),":[105],"greater":[107],"ranking":[108],"correlation":[109],"between":[110],"predicted":[111],"true":[113],"Spearman":[117],"$\u03c1$":[118],"Kendall":[120],"$\u03c4$)":[121],"across":[122],"range":[124],"benchmarks":[126],"binary":[129],"continuous":[131],"metrics.":[132],"Furthermore,":[133],"mRMR":[134],"subsampling":[135],"is":[136,153],"much":[137],"faster":[138],"than":[139],"competitor":[140],"(which":[142],"often":[143],"involve":[144],"fitting":[145],"probabilistic":[146],"models":[147],"or":[148,165],"running":[149],"clustering":[150],"algorithms),":[151],"more":[154],"likely":[155],"select":[157],"same":[159],"questions":[160],"under":[161],"different":[162],"random":[163],"seeds":[164],"training":[166],"data":[167],"splits.":[168],"Tutorial":[169],"code":[170],"found":[173],"https://github.com/sambowyer/mrmr_eval":[175],".":[176]},"counts_by_year":[],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2026-05-27T00:00:00"}
