{"id":9944,"date":"2020-09-24T09:06:00","date_gmt":"2020-09-24T09:06:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=9944"},"modified":"2023-11-09T14:12:15","modified_gmt":"2023-11-09T14:12:15","slug":"random-forest-definition","status":"publish","type":"post","link":"https:\/\/datascientest.com\/random-forest-definition","title":{"rendered":"Random Forest : For\u00eat d\u2019arbre de d\u00e9cision- D\u00e9finition et fonctionnement"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"9944\" class=\"elementor elementor-9944\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3c3f10e e-flex e-con-boxed e-con\" data-id=\"3c3f10e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-79c5b4a elementor-widget elementor-widget-text-editor\" data-id=\"79c5b4a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2>Une Random Forest (ou For\u00eat d&rsquo;arbres de d\u00e9cision en fran\u00e7ais) est une technique de <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\">Machine Learning<\/a> tr\u00e8s populaire aupr\u00e8s des Data Scientists et pour cause : elle pr\u00e9sente de nombreux avantages compar\u00e9 aux autres algorithmes de data.<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93f2b9b elementor-widget elementor-widget-text-editor\" data-id=\"93f2b9b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>C\u2019est une technique facile \u00e0 interpr\u00e9ter, stable, qui pr\u00e9sente en g\u00e9n\u00e9ral de bonnes accuracies et qui peut \u00eatre utilis\u00e9e pour des t\u00e2ches de r\u00e9gression ou de classification. Elle couvre donc une grande partie des probl\u00e8mes de <strong>Machine Learning<\/strong>.<\/p><p>Dans <strong>Random Forest<\/strong> il y a d\u2019abord le mot \u00ab\u00a0Forest\u00a0\u00bb (ou for\u00eat en fran\u00e7ais). On comprend donc que cet algorithme va reposer sur des arbres que l\u2019on appelle<b> arbre de d\u00e9cision<\/b> ou arbre d\u00e9cisionnel.<\/p>\t\t\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<div class=\"elementor-element elementor-element-c3f70de e-flex e-con-boxed e-con\" data-id=\"c3f70de\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c538894 elementor-widget elementor-widget-heading\" data-id=\"c538894\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">Un exemple d\u2019arbre de d\u00e9cision<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67e6c27 elementor-widget elementor-widget-text-editor\" data-id=\"67e6c27\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Comme son nom l\u2019indique, un arbre de d\u00e9cision aide le\u00a0<a href=\"https:\/\/datascientest.com\/formation-data-scientist\" target=\"_blank\" rel=\"noopener\">Data Scientist<\/a>\u00a0\u00e0 <b>prendre une d\u00e9cision <\/b>gr\u00e2ce \u00e0 une <b>s\u00e9rie de questions\u00a0<\/b> (aussi appel\u00e9es tests)<b> <\/b>dont la r\u00e9ponse (oui\/non)<b> m\u00e8nera \u00e0 la d\u00e9cision finale<\/b>.<\/p><p>Prenons un <b>exemple de classification binaire<\/b> : on cherche \u00e0 savoir si un champignon est comestible\u00a0 en fonction des <b>crit\u00e8res &#8211; ou features en anglais &#8211;<\/b>\u00a0suivants : couleur, taille du champignon, forme du chapeau, odeur, taille de la tige, pr\u00e9sence de t\u00e2ches, etc.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1f836e elementor-widget elementor-widget-image\" data-id=\"e1f836e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"446\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/unnamed-3.png\" class=\"attachment-medium_large size-medium_large wp-image-9946\" alt=\"arbre de d\u00e9cision\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/unnamed-3.png 512w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/unnamed-3-300x261.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Exemple d'arbre de d\u00e9cision<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cde6980 elementor-widget elementor-widget-image\" data-id=\"cde6980\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"835\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/Arbre-Random-Forest-768x835.png\" class=\"attachment-medium_large size-medium_large wp-image-9947\" alt=\"random forest\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/Arbre-Random-Forest-768x835.png 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/Arbre-Random-Forest-276x300.png 276w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/Arbre-Random-Forest-941x1024.png 941w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/09\/Arbre-Random-Forest-1412x1536.png 1412w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Sch\u00e9ma d'arbre de d\u00e9cision<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e0bd18 elementor-widget elementor-widget-text-editor\" data-id=\"8e0bd18\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<blockquote><p><em>Sur l\u2019arbre, <b>chaque question correspond \u00e0 un noeud <\/b>c\u2019est-\u00e0-dire \u00e0 <b>un endroit o\u00f9 une branche se s\u00e9pare en deux branches<\/b>. En fonction de la r\u00e9ponse \u00e0 chaque question, nous allons nous orienter vers telle ou telle branche de l\u2019arbre pour finalement <b>arriver sur une feuille <\/b>de l\u2019arbre (ou extr\u00e9mit\u00e9) qui contiendra la r\u00e9ponse \u00e0 notre question.<\/em><\/p><\/blockquote>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b05ce9 elementor-widget elementor-widget-text-editor\" data-id=\"0b05ce9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Vous vous demandez peut-\u00eatre comment on choisit l\u2019ordre des questions \u00e0 poser : <strong>pourquoi commencer par telle ou telle question ?\u00a0<\/strong><\/p><p>A chaque n\u0153ud, l\u2019algorithme se pose la question de savoir <strong>quelle question poser<\/strong> c\u2019est-\u00e0-dire si on doit plut\u00f4t s\u2019int\u00e9resser \u00e0 l\u2019odeur, la forme du chapeau ou la taille du champignon. Il va donc calculer pour chaque feature le <strong>gain d\u2019information<\/strong> que l\u2019on obtiendrait si l\u2019on choisissait cette feature. Nous voulons maximiser le gain d\u2019information c\u2019est pourquoi l\u2019arbre choisit la question et donc la <strong>feature qui maximise ce gain<\/strong>.<\/p>\t\t\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<div class=\"elementor-element elementor-element-d27bca2 e-flex e-con-boxed e-con\" data-id=\"d27bca2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e0de8d elementor-widget elementor-widget-heading\" data-id=\"1e0de8d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">La for\u00eat comme la combinaison des arbres<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-400f845 elementor-widget elementor-widget-text-editor\" data-id=\"400f845\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Random Forest<\/strong> est ce qu\u2019on appelle une <strong>m\u00e9thode d\u2019ensemble<\/strong> (ou ensemble method en anglais) c\u2019est-\u00e0-dire qu\u2019elle \u00ab\u00a0met ensemble\u00a0\u00bb ou <b>combine des r\u00e9sultats<\/b> pour obtenir un super r\u00e9sultat final.\u00a0<\/p><p>Mais les r\u00e9sultats de quoi ? Tout simplement des diff\u00e9rents arbres de d\u00e9cision qui la composent.<\/p><p>Les <b>Random Forest <\/b>peuvent \u00eatre compos\u00e9es de <b>plusieurs dizaines voire centaines d\u2019arbres, le nombre d\u2019arbre est un param\u00e8tre que l\u2019on ajuste g\u00e9n\u00e9ralement par validation crois\u00e9e <\/b>(ou cross-validation en anglais). Pour faire court, la validation crois\u00e9e est une technique d&rsquo;\u00e9valuation d&rsquo;un algorithme de <b>Machine Learning <\/b>consistant \u00e0 entrainer et tester le mod\u00e8le sur des morceaux du <b>dataset<\/b> de d\u00e9part.<\/p><blockquote><p><b>Chaque arbre est entra\u00een\u00e9 sur un sous-ensemble <\/b>du <strong>dataset<\/strong> et donne un r\u00e9sultat (oui ou non dans le cas de notre exemple sur les champignons). Les <b>r\u00e9sultats de tous les arbres<\/b> de d\u00e9cision sont alors <b>combin\u00e9s <\/b>pour donner une r\u00e9ponse finale. <b>Chaque arbre \u00ab\u00a0vote\u00a0\u00bb <\/b>(oui ou non) et la <b>r\u00e9ponse finale est celle qui a eu la majorit\u00e9 de vote<\/b>.<\/p><\/blockquote><p>C\u2019est ce que l\u2019on appelle une <b>m\u00e9thode de bagging<\/b> :\u00a0\u00a0<\/p><ul><li>On <b>d\u00e9coupe<\/b> notre <strong>dataset<\/strong> en plusieurs <b>sous-ensembles al\u00e9atoirement constitu\u00e9s d\u2019\u00e9chantillons<\/b> &#8211; d\u2019o\u00f9 le \u00ab\u00a0Random\u00a0\u00bb dans Random Forest.<\/li><li>On <b>entraine un mod\u00e8le sur chaque sous-ensemble<\/b> : il y a autant de mod\u00e8les que de sous-ensembles.\u00a0<\/li><li>On <b>combine tous les r\u00e9sultats des mod\u00e8les <\/b>(avec un syst\u00e8me de vote par exemple) ce qui nous donne un r\u00e9sultat final.<\/li><\/ul><p>De cette mani\u00e8re on construit un<b> mod\u00e8le robuste <\/b>\u00e0 partir de plusieurs mod\u00e8les qui sont pas forc\u00e9ment aussi robustes.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84536b2 elementor-widget elementor-widget-text-editor\" data-id=\"84536b2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>En bref, cet algorithme est tr\u00e8s populaire pour sa capacit\u00e9 \u00e0 <b>combiner les r\u00e9sultats de ses arbres<\/b> pour obtenir un<b> r\u00e9sultat final plus fiable<\/b>. Son efficacit\u00e9 lui a permis d\u2019\u00eatre utilis\u00e9 dans de nombreux domaines comme par exemple le <b>marketing<\/b> t\u00e9l\u00e9phonique pour pr\u00e9dire le comportement de clients ou encore la <b>finance<\/b> pour la <b>gestion de risques<\/b>.<\/p><p>Vous souhaitez d\u00e9marrer une formation en Machine Learning ? D\u00e9couvrez vite nos cursus\u00a0<a href=\"https:\/\/datascientest.com\/formation-data-analyst\" target=\"_blank\" rel=\"noopener\">Data Analyst<\/a>,\u00a0<a href=\"https:\/\/datascientest.com\/formation-data-scientist\" target=\"_blank\" rel=\"noopener\">Data Scientist<\/a>\u00a0et\u00a0<a href=\"https:\/\/datascientest.com\/formation-data-engineer\" target=\"_blank\" rel=\"noopener\">Data Engineer<\/a>\u00a0!\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4983f01 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"4983f01\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/rdv\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Prendre rendez-vous en ligne<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Une Random Forest (ou For\u00eat d&rsquo;arbres de d\u00e9cision en fran\u00e7ais) est une technique de Machine Learning tr\u00e8s populaire aupr\u00e8s des Data Scientists et pour cause : elle pr\u00e9sente de nombreux avantages compar\u00e9 aux autres algorithmes de data. C\u2019est une technique facile \u00e0 interpr\u00e9ter, stable, qui pr\u00e9sente en g\u00e9n\u00e9ral de bonnes accuracies et qui peut \u00eatre [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":10322,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Random Forest : For\u00eat d\u2019arbre de d\u00e9cision- D\u00e9finition et fonctionnement<\/title>\n<meta name=\"description\" content=\"Qu&#039;est ce qu&#039;une random forest ? 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