{"id":"https://openalex.org/W4391584324","doi":"https://doi.org/10.1145/3631802.3631808","title":"Common Errors in Machine Learning Projects: A Second Look","display_name":"Common Errors in Machine Learning Projects: A Second Look","publication_year":2023,"publication_date":"2023-11-13","ids":{"openalex":"https://openalex.org/W4391584324","doi":"https://doi.org/10.1145/3631802.3631808"},"language":"en","primary_location":{"id":"doi:10.1145/3631802.3631808","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3631802.3631808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd Koli Calling International Conference on Computing Education Research","raw_type":"proceedings-article"},"type":"article","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/A5101728355","display_name":"Renato Magela Zimmermann","orcid":"https://orcid.org/0009-0008-7774-3167"},"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":true,"raw_author_name":"Renato Magela Zimmermann","raw_affiliation_strings":["University of Toronto, Canada"],"raw_orcid":"https://orcid.org/0009-0008-7774-3167","affiliations":[{"raw_affiliation_string":"University of Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042191480","display_name":"Sonya Allin","orcid":"https://orcid.org/0000-0001-6611-2265"},"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":"Sonya Allin","raw_affiliation_strings":["University of Toronto Mississauga, Canada"],"raw_orcid":"https://orcid.org/0000-0001-6611-2265","affiliations":[{"raw_affiliation_string":"University of Toronto Mississauga, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068203497","display_name":"Lisa Zhang","orcid":"https://orcid.org/0000-0002-7302-6530"},"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":"Lisa Zhang","raw_affiliation_strings":["University of Toronto Mississauga, Canada"],"raw_orcid":"https://orcid.org/0000-0002-7302-6530","affiliations":[{"raw_affiliation_string":"University of Toronto Mississauga, Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101728355"],"corresponding_institution_ids":["https://openalex.org/I185261750"],"apc_list":null,"apc_paid":null,"fwci":0.6816,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77049407,"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":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.995199978351593,"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":0.995199978351593,"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/T11891","display_name":"Big Data and Business Intelligence","score":0.9650999903678894,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.960099995136261,"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/computer-science","display_name":"Computer science","score":0.6734362840652466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47585198283195496},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4127492904663086}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6734362840652466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47585198283195496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4127492904663086}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3631802.3631808","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3631802.3631808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd Koli Calling International Conference on Computing Education Research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W598355015","https://openalex.org/W1991613282","https://openalex.org/W2008107570","https://openalex.org/W2056825598","https://openalex.org/W2146141698","https://openalex.org/W2250706791","https://openalex.org/W2357927175","https://openalex.org/W2587903488","https://openalex.org/W2736287575","https://openalex.org/W2766624855","https://openalex.org/W2787225861","https://openalex.org/W2804535652","https://openalex.org/W2915975654","https://openalex.org/W2964325521","https://openalex.org/W2964327531","https://openalex.org/W2974036012","https://openalex.org/W2997618083","https://openalex.org/W3029022390","https://openalex.org/W3104128335","https://openalex.org/W3173795738","https://openalex.org/W3176067521","https://openalex.org/W3177809140","https://openalex.org/W4210360375","https://openalex.org/W4293227336","https://openalex.org/W4380319022","https://openalex.org/W4389676498"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"While":[0],"machine":[1,79,98,299],"learning":[2,80,99,300],"(ML)":[3],"has":[4],"proved":[5],"impactful":[6],"in":[7,13,90,95,266],"many":[8],"disciplines,":[9],"design":[10,141,214],"decisions":[11,315],"involved":[12],"building":[14],"ML":[15,40,47,281],"models":[16,115,237],"are":[17,253,263],"difficult":[18],"for":[19,108,198,220],"novices":[20],"to":[21,54,72,87,130,139,153,168,276,297,304],"make,":[22],"and":[23,59,125,180,206,250,283,312],"mistakes":[24],"can":[25],"cause":[26],"harm.":[27],"Prior":[28],"work":[29,63,71,267],"by":[30,39,78,268,274],"Skripchuk":[31,194],"et":[32,195],"al.":[33,196],"[35]":[34],"identified":[35],"common":[36,75,171,218],"errors":[37,76,89,142,179,187,247,252,262],"made":[38,77],"students":[41,113,145,223,275],"via":[42,143],"qualitative":[43],"analysis":[44],"of":[45,69,227,231,246,260,288,295],"open-ended":[46],"assessments,":[48],"but":[49],"their":[50,147],"sample":[51],"was":[52],"limited":[53],"a":[55,84,109,117,166,272],"single":[56],"institution,":[57],"course,":[58],"assessment":[60],"setting.":[61],"Our":[62,128,149],"is":[64,152],"an":[65,96,160],"extended,":[66],"conceptual":[67],"replication":[68],"this":[70],"understand":[73],"the":[74,105,155,175,181,285,289],"students.":[81],"We":[82,241,292],"use":[83,224],"mixed-method":[85],"approach":[86],"analyze":[88,131],"30":[91],"final":[92,102],"project":[93,132,150],"reports":[94,103,133],"undergraduate":[97],"course.":[100],"The":[101],"describe":[104],"model-building":[106],"process":[107],"classification":[110],"task,":[111],"where":[112,222],"build":[114],"on":[116,159,213],"complex":[118],"data":[119,202,305,307,314],"set":[120],"with":[121,193,201,235,255],"numerical,":[122],"categorical,":[123],"ordinal":[124],"text":[126],"features.":[127],"choice":[129],"(rather":[134],"than":[135],"code)":[136],"allows":[137],"us":[138],"uncover":[140],"how":[144],"justify":[146],"methodology.":[148],"task":[151],"achieve":[154,284],"best":[156],"test":[157,162,183,257],"accuracy":[158,184],"unseen":[161],"set;":[163],"thus,":[164],"as":[165],"way":[167],"validate":[169],"these":[170,178,243,261],"errors,":[172,219],"we":[173,188],"identify":[174,293],"association":[176],"between":[177,280],"model\u2019s":[182],"performance.":[185],"Common":[186],"find":[189],"include":[190],"those":[191],"consistent":[192],"[35],":[197],"example":[199,221],"issues":[200],"processing,":[203,306],"hyperparameter":[204],"tuning,":[205],"model":[207,248,317],"selection.":[208],"In":[209],"addition,":[210],"our":[211],"focus":[212],"error":[215],"exposes":[216],"other":[217],"certain":[225],"kinds":[226],"features":[228],"(e.g.,":[229,238],"bag":[230],"words":[232],"representations)":[233],"only":[234],"particular":[236],"Naive":[239],"Bayes).":[240],"call":[242],"latter":[244],"types":[245],"misconceptions,":[249],"such":[251],"associated":[254],"lower":[256],"accuracy.":[258],"Some":[259],"also":[264],"present":[265],"practitioners.":[269],"Others":[270],"reflect":[271],"difficulty":[273],"make":[277],"correct":[278],"connections":[279],"concepts":[282],"relational":[286],"level":[287],"SOLO":[290],"taxonomy.":[291],"areas":[294],"opportunity":[296],"improve":[298],"pedagogy,":[301],"particularly":[302],"related":[303],"leakage,":[308],"hyperparameters,":[309],"nonsensical":[310],"outputs,":[311],"disentangling":[313],"from":[316],"decisions.":[318]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
