{"id":"https://openalex.org/W4313400978","doi":"https://doi.org/10.1007/978-981-19-5170-1_8","title":"Case Study I: Tuning Random Forest (Ranger)","display_name":"Case Study I: Tuning Random Forest (Ranger)","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4313400978","doi":"https://doi.org/10.1007/978-981-19-5170-1_8"},"language":"en","primary_location":{"id":"doi:10.1007/978-981-19-5170-1_8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-981-19-5170-1_8","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-981-19-5170-1_8.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Hyperparameter Tuning for Machine and Deep Learning with R","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/978-981-19-5170-1_8.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020539256","display_name":"Thomas Bartz\u2013Beielstein","orcid":"https://orcid.org/0000-0002-5938-5158"},"institutions":[{"id":"https://openalex.org/I102520234","display_name":"TH K\u00f6ln - University of Applied Sciences","ror":"https://ror.org/014nnvj65","country_code":"DE","type":"education","lineage":["https://openalex.org/I102520234"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Thomas Bartz-Beielstein","raw_affiliation_strings":["Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany","institution_ids":["https://openalex.org/I102520234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063401368","display_name":"Sowmya Chandrasekaran","orcid":"https://orcid.org/0000-0002-5304-6411"},"institutions":[{"id":"https://openalex.org/I102520234","display_name":"TH K\u00f6ln - University of Applied Sciences","ror":"https://ror.org/014nnvj65","country_code":"DE","type":"education","lineage":["https://openalex.org/I102520234"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sowmya Chandrasekaran","raw_affiliation_strings":["Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany","institution_ids":["https://openalex.org/I102520234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074567485","display_name":"Frederik Rehbach","orcid":"https://orcid.org/0000-0003-0922-8629"},"institutions":[{"id":"https://openalex.org/I102520234","display_name":"TH K\u00f6ln - University of Applied Sciences","ror":"https://ror.org/014nnvj65","country_code":"DE","type":"education","lineage":["https://openalex.org/I102520234"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Frederik Rehbach","raw_affiliation_strings":["Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany","institution_ids":["https://openalex.org/I102520234"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029388735","display_name":"Martin Zaefferer","orcid":"https://orcid.org/0000-0003-2372-2092"},"institutions":[{"id":"https://openalex.org/I102520234","display_name":"TH K\u00f6ln - University of Applied Sciences","ror":"https://ror.org/014nnvj65","country_code":"DE","type":"education","lineage":["https://openalex.org/I102520234"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Zaefferer","raw_affiliation_strings":["Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany","Bartz & Bartz GmbH and with Institute for Data Science, Engineering, and\u00a0Analytics, TH\u00a0K\u00f6ln, Gummersbach, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Data Science, Engineering and Analytics, TH K\u00f6ln, Cologne, Germany","institution_ids":["https://openalex.org/I102520234"]},{"raw_affiliation_string":"Bartz & Bartz GmbH and with Institute for Data Science, Engineering, and\u00a0Analytics, TH\u00a0K\u00f6ln, Gummersbach, Germany","institution_ids":["https://openalex.org/I102520234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5020539256"],"corresponding_institution_ids":["https://openalex.org/I102520234"],"apc_list":null,"apc_paid":null,"fwci":1.4964,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81079187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"187","last_page":"220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.8418999910354614,"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":0.8418999910354614,"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/T10320","display_name":"Neural Networks and Applications","score":0.8228999972343445,"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/random-forest","display_name":"Random forest","score":0.7288849353790283},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7025934457778931},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.681816577911377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6543517708778381},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5800976753234863},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5708909630775452},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5616203546524048},{"id":"https://openalex.org/keywords/cart","display_name":"Cart","score":0.5298286080360413},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.5184571146965027},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.49216097593307495},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4857774078845978},{"id":"https://openalex.org/keywords/r-package","display_name":"R package","score":0.45032042264938354},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.44409313797950745},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42445090413093567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4149773418903351},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3603591322898865},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35076969861984253},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2498263418674469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1891939342021942},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15335315465927124},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1371554434299469},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.08816766738891602}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7288849353790283},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7025934457778931},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.681816577911377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543517708778381},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5800976753234863},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5708909630775452},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5616203546524048},{"id":"https://openalex.org/C2777275308","wikidata":"https://www.wikidata.org/wiki/Q234668","display_name":"Cart","level":2,"score":0.5298286080360413},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.5184571146965027},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49216097593307495},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4857774078845978},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.45032042264938354},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.44409313797950745},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42445090413093567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4149773418903351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3603591322898865},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35076969861984253},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2498263418674469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1891939342021942},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15335315465927124},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1371554434299469},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.08816766738891602},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C129307140","wikidata":"https://www.wikidata.org/wiki/Q6795880","display_name":"Maximum bubble pressure method","level":3,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C157915830","wikidata":"https://www.wikidata.org/wiki/Q2928001","display_name":"Bubble","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-981-19-5170-1_8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-981-19-5170-1_8","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-981-19-5170-1_8.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Hyperparameter Tuning for Machine and Deep Learning with R","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-981-19-5170-1_8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-981-19-5170-1_8","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-981-19-5170-1_8.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Hyperparameter Tuning for Machine and Deep Learning with R","raw_type":"book-chapter"},"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4313400978.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4295309597","https://openalex.org/W4322775603","https://openalex.org/W4210794429","https://openalex.org/W4362544620","https://openalex.org/W3096565539","https://openalex.org/W4223456145","https://openalex.org/W4317600379","https://openalex.org/W4320494184","https://openalex.org/W4200551482","https://openalex.org/W4375930479"],"abstract_inverted_index":{"Abstract":[0],"This":[1,59],"case":[2,64],"study":[3],"gives":[4],"a":[5,97,129,156,177],"hands-on":[6],"description":[7],"of":[8,32,141,162],"Hyperparameter":[9],"Tuning":[10],"(HPT)":[11],"methods":[12,161],"discussed":[13],"in":[14,36,110,176],"this":[15],"book.":[16],"The":[17,91,148,170],"Random":[18],"Forest":[19],"(RF)":[20],"method":[21,31],"and":[22,47,60,77,114,125,165],"its":[23],"implementation":[24],"was":[25],"chosen":[26],"because":[27],"it":[28],"is":[29,43,75,82,94,134,153,174],"the":[30,33,61,67,72,79,86,101,104,118,138,142,160,163,167],"first":[34],"choice":[35],"many":[37],"Machine":[38],"Learning":[39],"(ML)":[40],"tasks.":[41],"RF":[42],"easy":[44],"to":[45,99,112,136,166],"implement":[46],"robust.":[48],"It":[49],"can":[50],"handle":[51],"continuous":[52],"as":[53,55,96,155],"well":[54,149],"discrete":[56],"input":[57],"variables.":[58],"following":[62],"two":[63],"studies":[65],"follow":[66],"same":[68],"HPT":[69,87,105],"pipeline:":[70],"after":[71],"data":[73],"set":[74,83],"provided":[76],"pre-processed,":[78],"experimental":[80],"design":[81],"up.":[84],"Next,":[85],"experiments":[88],"are":[89],"performed.":[90],"R":[92,151],"package":[93,152],"used":[95,135,154],"\u201cdatascope\u201d":[98],"analyze":[100],"results":[102,121,143],"from":[103,107,122,144,159],"runs":[106],"several":[108],"perspectives:":[109],"addition":[111],"Classification":[113],"Regression":[115],"Trees":[116],"(CART),":[117],"analysis":[119],"combines":[120],"surface,":[123],"sensitivity":[124],"parallel":[126],"plots":[127],"with":[128],"classical":[130],"regression":[131],"analysis.":[132],"Severity":[133],"discuss":[137],"practical":[139],"relevance":[140],"an":[145],"error-statistical":[146],"point-of-view.":[147],"proven":[150],"uniform":[157],"interface":[158],"packages":[164],"ML":[168],"methods.":[169],"corresponding":[171],"source":[172],"code":[173],"explained":[175],"comprehensible":[178],"manner.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
