{"id":"https://openalex.org/W4254465048","doi":"https://doi.org/10.1201/9780429341830-29","title":"Cross validation","display_name":"Cross validation","publication_year":2019,"publication_date":"2019-11-12","ids":{"openalex":"https://openalex.org/W4254465048","doi":"https://doi.org/10.1201/9780429341830-29"},"language":"en","primary_location":{"id":"doi:10.1201/9780429341830-29","doi":"https://doi.org/10.1201/9780429341830-29","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9780429341830-29","pdf_url":null,"source":{"id":"https://openalex.org/S4306467597","display_name":"Chapman & Hall/CRC eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320449","host_organization_name":"Informa","host_organization_lineage":["https://openalex.org/P4310320449"],"host_organization_lineage_names":["Informa"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Introduction to Data Science","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W2985957070","display_name":"Introduction to Data Science"},"type":"book-chapter","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054612687","display_name":"Rafael A. Irizarry","orcid":"https://orcid.org/0000-0002-3944-4309","observed_orcids":["https://orcid.org/0000-0002-3944-4309"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rafael A. Irizarry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5054612687"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.518,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.69704264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"507","last_page":"522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5835000276565552,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.5835000276565552,"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/T10136","display_name":"Statistical Methods and Inference","score":0.16060000658035278,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.06260000169277191,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cross-validation","display_name":"cross-validation","score":0.996999979019165},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.8169999718666077},{"id":"https://openalex.org/keywords/k-fold-cross-validation","display_name":"k-fold cross-validation","score":0.6129999756813049},{"id":"https://openalex.org/keywords/training-data","display_name":"training data","score":0.6259999871253967},{"id":"https://openalex.org/keywords/test-set","display_name":"test set","score":0.5019999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35836973786354065}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/9780429341830-29","doi":"https://doi.org/10.1201/9780429341830-29","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9780429341830-29","pdf_url":null,"source":{"id":"https://openalex.org/S4306467597","display_name":"Chapman & Hall/CRC eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320449","host_organization_name":"Informa","host_organization_lineage":["https://openalex.org/P4310320449"],"host_organization_lineage_names":["Informa"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Introduction to Data Science","raw_type":"book-chapter"}],"best_oa_location":null,"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/W2107340281","https://openalex.org/W7093346512","https://openalex.org/W4387904993","https://openalex.org/W2998678080","https://openalex.org/W4236492481","https://openalex.org/W7100220670","https://openalex.org/W4206802975","https://openalex.org/W2040615655","https://openalex.org/W2080951248","https://openalex.org/W7160496229"],"abstract_inverted_index":{"In":[0],"this":[1],"chapter,":[2],"the":[3,10,27,35,39,49,92,99,108,116,124,131,135,141,145,147],"authors":[4,40],"introduce":[5,34],"cross":[6,22,63,85],"validation,":[7,86],"one":[8],"of":[9,44,62,76,91,101,115,119,133],"most":[11],"important":[12],"ideas":[13],"in":[14,24],"machine":[15],"learning.":[16],"They":[17],"describes":[18],"how":[19],"to":[20,66,89,110,122],"implement":[21],"validation":[23,64,127],"practice":[25],"with":[26,144],"caret":[28],"package":[29],"later.":[30],"However,":[31],"once":[32],"they":[33,53],"training":[36],"set":[37,75],"(right),":[38],"see":[41],"that":[42],"many":[43,102],"small":[45],"islands":[46],"now":[47],"have":[48],"opposite":[50],"color":[51],"and":[52,74],"end":[54],"up":[55],"making":[56],"several":[57],"incorrect":[58],"predictions.":[59],"The":[60],"goal":[61],"is":[65,128],"estimate":[67],"these":[68],"quantities":[69],"for":[70],"any":[71],"given":[72],"algorithm":[73,109],"tuning":[77],"parameters":[78],"such":[79],"as":[80,98,139],"k":[81],".":[82],"To":[83],"understand":[84],"it":[87],"helps":[88],"think":[90],"true":[93],"error,":[94],"a":[95],"theoretical":[96,136],"quantity,":[97],"average":[100],"apparent":[103],"errors":[104],"obtained":[105],"by":[106],"applying":[107],"B":[111],"new":[112],"random":[113],"samples":[114],"data,":[117],"none":[118],"them":[120],"used":[121],"train":[123],"algorithm.":[125],"Cross":[126],"based":[129],"on":[130],"idea":[132],"imitating":[134],"setup":[137],"above":[138],"best":[140],"people":[142,148],"can":[143],"data":[146],"have.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2022-05-12T00:00:00"}