{"id":"https://openalex.org/W3158155751","doi":null,"title":"Interpretable Machine Learning with Python","display_name":"Interpretable Machine Learning with Python","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3158155751","mag":"3158155751"},"language":"en","primary_location":{"id":"pmh:oai:cds.cern.ch:2763167","doi":null,"is_oa":false,"landing_page_url":"http://cds.cern.ch/record/2763167","pdf_url":null,"source":{"id":"https://openalex.org/S4306400735","display_name":"International Linear Collider","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5044546350","display_name":"Serg Masis","orcid":"https://orcid.org/0000-0002-2010-9498","observed_orcids":["https://orcid.org/0000-0002-2010-9498"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Serg Masis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5044546350"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8762,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.74261977,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":1.0,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":1.0,"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":0.0,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/model-interpretability","display_name":"model interpretability","score":0.8809999823570251},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.9760000109672546},{"id":"https://openalex.org/keywords/black-box-modeling","display_name":"black-box models","score":0.8730000257492065},{"id":"https://openalex.org/keywords/white-box-models","display_name":"white-box models","score":0.9089999794960022},{"id":"https://openalex.org/keywords/model-agnostic-explanation","display_name":"model-agnostic explanation","score":0.6430000066757202},{"id":"https://openalex.org/keywords/bias-mitigation","display_name":"bias mitigation","score":0.6299999952316284},{"id":"https://openalex.org/keywords/monotonicity-constraints","display_name":"monotonicity constraints","score":0.8949999809265137},{"id":"https://openalex.org/keywords/adversarial-retraining","display_name":"adversarial retraining","score":0.9419999718666077},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.9829999804496765},{"id":"https://openalex.org/keywords/model-reliability","display_name":"model reliability","score":0.6480000019073486},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6800000071525574},{"id":"https://openalex.org/keywords/algorithmic-fairness","display_name":"algorithmic fairness","score":0.4339999854564667}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9085177779197693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7821465134620667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.729711651802063},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.708013653755188},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6859007477760315},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6669521927833557},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.5833132266998291},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5760838985443115},{"id":"https://openalex.org/C180932941","wikidata":"https://www.wikidata.org/wiki/Q997233","display_name":"White box","level":2,"score":0.5562540888786316},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1231917142868042},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:cds.cern.ch:2763167","doi":null,"is_oa":false,"landing_page_url":"http://cds.cern.ch/record/2763167","pdf_url":null,"source":{"id":"https://openalex.org/S4306400735","display_name":"International Linear Collider","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"mag:3158155751","doi":null,"is_oa":false,"landing_page_url":"https://skills.scholarvox.com/catalog/book/88911892?_locale=en","pdf_url":null,"source":{"id":"https://openalex.org/S4306477403","display_name":"Packt Publishing eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"ebook platform"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Packt Publishing eBooks","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7038388623","https://openalex.org/W2973631113","https://openalex.org/W3081238989","https://openalex.org/W4322621694","https://openalex.org/W7019859633","https://openalex.org/W7038742213","https://openalex.org/W3105269729","https://openalex.org/W6912167837","https://openalex.org/W3178050204","https://openalex.org/W4200552742"],"abstract_inverted_index":{"Understand":[0],"the":[1,104,189,192,196,205,250,272,311,358,382],"key":[2,120],"aspects":[3,121],"and":[4,18,25,48,75,117,122,135,138,165,214,226,239,246,263,289,304,331,346,369,373,381],"challenges":[5],"of":[6,103,155,252,274,313,378],"machine":[7,37,83,343,379],"learning":[8,38,62,84,344,380],"interpretability,":[9,111],"learn":[10],"how":[11,30,63,127,166,299,308,317,357,370],"to":[12,21,31,44,64,71,110,133,149,167,170,188,198,243,258,309,318,328,355],"overcome":[13],"them":[14,20,132,169,265],"with":[15,41,79,90,97,151,211,296,323],"interpretation":[16,156],"methods,":[17,157,164],"leverage":[19],"build":[22,45],"fairer,":[23,46],"safer,":[24,47],"more":[26,49,321],"reliable":[27,50,322],"modelsKey":[28],"FeaturesLearn":[29],"extract":[32],"easy-to-understand":[33],"insights":[34],"from":[35,235],"any":[36],"modelBecome":[39],"well-versed":[40,292],"interpretability":[42,218,267,275],"techniques":[43],"modelsMitigate":[51],"risks":[52,77],"in":[53,115,276,293,315],"AI":[54,162,359],"systems":[55,360],"before":[56],"they":[57,361,371],"have":[58,350],"broader":[59],"implications":[60],"by":[61,219],"debug":[65],"black-box":[66,134],"modelsBook":[67],"DescriptionDo":[68],"you":[69,94,147,269],"want":[70],"understand":[72,259],"your":[73],"models":[74,129,213,261,278,295,320],"mitigate":[76,310],"associated":[78],"poor":[80],"predictions":[81],"using":[82,202],"(ML)":[85],"interpretation?":[86],"Interpretable":[87],"Machine":[88],"Learning":[89],"Python":[91,383],"can":[92],"help":[93],"work":[95],"effectively":[96],"ML":[98,260],"models.":[99],"The":[100,142,229],"first":[101],"section":[102,144],"book":[105,193,335,338],"is":[106,336,339,386],"a":[107,152],"beginner's":[108],"guide":[109],"covering":[112],"its":[113,119],"relevance":[114],"business":[116],"exploring":[118],"challenges.":[123],"You'll":[124],"focus":[125],"on":[126,366],"white-box":[128],"work,":[130,363],"compare":[131],"glass-box":[136],"models,":[137,286],"examine":[139],"their":[140,364],"trade-off.":[141],"second":[143],"will":[145,270],"get":[146,209],"up":[148],"speed":[150],"vast":[153],"array":[154],"also":[158,194],"known":[159],"as":[160,284],"Explainable":[161],"(XAI)":[163],"apply":[168],"different":[171],"use":[172],"cases,":[173],"be":[174,256],"it":[175,306],"for":[176,180,217,340],"classification":[177],"or":[178,184],"regression,":[179],"tabular,":[181],"time-series,":[182],"image":[183,301],"text.":[185],"In":[186,204],"addition":[187],"step-by-step":[190],"code,":[191],"helps":[195],"reader":[197],"interpret":[199],"model":[200],"outcomes":[201],"examples.":[203],"third":[206],"section,":[207],"you'll":[208,231,255],"hands-on":[210],"tuning":[212],"training":[215],"data":[216,341,347],"reducing":[220],"complexity,":[221],"mitigating":[222],"bias,":[223],"placing":[224],"guardrails,":[225],"enhancing":[227],"reliability.":[228],"methods":[230,242],"explore":[232],"here":[233],"range":[234],"state-of-the-art":[236],"feature":[237],"selection":[238],"dataset":[240],"debiasing":[241],"monotonic":[244,326],"constraints":[245,327],"adversarial":[247,324],"retraining.":[248],"By":[249],"end":[251],"this":[253,334],"book,":[254],"able":[257],"better":[262],"enhance":[264],"through":[266],"tuning.What":[268],"learnRecognize":[271],"importance":[273],"businessStudy":[277],"that":[279],"are":[280],"intrinsically":[281],"interpretable":[282],"such":[283],"linear":[285],"decision":[287,367],"trees,":[288],"Naive":[290],"BayesBecome":[291],"interpreting":[294],"model-agnostic":[297],"methodsVisualize":[298],"an":[300,351],"classifier":[302],"works":[303],"what":[305],"learnsUnderstand":[307],"influence":[312],"bias":[314],"datasetsDiscover":[316],"make":[319,329],"robustnessUse":[325],"fairer":[330],"safer":[332],"modelsWho":[333],"forThis":[337],"scientists,":[342],"developers,":[345],"stewards":[348],"who":[349],"increasingly":[352],"critical":[353],"responsibility":[354],"explain":[356],"develop":[362],"impact":[365],"making,":[368],"identify":[372],"manage":[374],"bias.":[375],"Working":[376],"knowledge":[377],"programming":[384],"language":[385],"expected.":[387]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-05-10T00:00:00"}