{"id":4197,"date":"2020-11-15T16:04:49","date_gmt":"2020-11-15T16:04:49","guid":{"rendered":"https:\/\/detralytics.com\/?p=4197"},"modified":"2026-06-22T14:49:52","modified_gmt":"2026-06-22T14:49:52","slug":"features-with-flat-partial-dependence-plots-up-to-a-certain-level-not-important","status":"publish","type":"post","link":"https:\/\/detralytics.com\/en\/publications\/features-with-flat-partial-dependence-plots-up-to-a-certain-level-not-important\/","title":{"rendered":"Features with flat partial dependence plots up to a certain level: not important?"},"content":{"rendered":"\t\t
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Features with flat partial dependence plots up to a certain level: not important?<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2020-4<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis paper focuses on partial dependence plots which are often used when modeling with machine learning techniques in order to better understand the effects of the features on the conditional expectation of the response variable. However, these plots must be interpreted with caution. Indeed, they can easily lead to wrong interpretations in case the analyst is not enough familiar with these plots. As noticed in a previous FAQctuary, a typical situation is the case where a feature is important because of its interactions with others while its partial dependence plot is flat. In this FAQctuary, we go one step further and we consider a very simple example with a three-way interaction effect and we show that only looking at partial dependence plots for each feature and for two features may indeed lead the analyst to wrong conclusions.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Sector: <\/b>Insurance<\/p>\n

<\/b>Expertise: <\/b>Machine learning<\/p>\n

Authors:<\/b> Elke Gagelmans,<\/p>\n

Michel Denuit and Julien Trufin<\/p>\n

\u00a0<\/p>\n

Publisher:<\/b> Detralytics<\/p>\n

Date:<\/b> November 2020<\/p>\n

Language:<\/b> English<\/p>\n

Pages:<\/b> 5<\/p>\n

Reference :<\/b> FAQctuary 2020-2<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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About the authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Elke Gagelmans<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Elke Gagelmans is currently an Actuary at KBC Bank & Verzekering. Elke Gagelmans brings experience from previous roles at Detralytics, Slaagsleutels and EY. Elke Gagelmans holds a 2018 – 2019 Master’s degree in actuarial and financial engineering @ KU Leuven.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Michel\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Michel Denuit<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Michel is a Scientific Advisor at Detralytics, as well as a professor in actuarial science at the Universit\u00e9 Catholique de Louvain. He has international experience as a visiting professor, and has promoted many projects in collaboration with the industry. At Detralytics, Michel coaches young talents, provides cutting-edge training, fosters innovation and oversees R&D projects.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Julien\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Julien Trufin<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Julien is a Scientific Advisor at Detralytics, as well as a professor in Actuarial Science at the department of mathematics of the Universit\u00e9 Libre de Bruxelles. Julien has experience as a consultant and a strong academic background developed at prominent institutions, including Universit\u00e9 Laval (Canada), UCL, and ULB (Belgium). At Detralytics, Julien coaches young talents, provide cutting-edge training, fosters innovation and oversees R&D projects.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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This paper focuses on partial dependence plots which are often used when modeling with machine learning techniques in order to better understand the effects of the features on the conditional expectation of the response variable.<\/p>\n","protected":false},"author":1,"featured_media":1971,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[138,133],"tags":[132],"class_list":["post-4197","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-138","category-publications","tag-faqctuary"],"yoast_head":"\nFAQctuary : Features with flat partial dependence<\/title>\n<meta name=\"description\" content=\"This paper focuses on partial dependence plots which are often used when modeling with machine learning techniques in order to better understand the effects of the features on the conditional expectation of the response variable.\" \/>\n<meta name=\"robots\" content=\"index, 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