{"id":202007,"date":"2023-10-04T07:30:00","date_gmt":"2023-10-04T07:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=202007"},"modified":"2023-11-09T13:41:46","modified_gmt":"2023-11-09T13:41:46","slug":"ensembling-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/ensembling-tout-savoir","title":{"rendered":"Ensembling : Qu&rsquo;est-ce que c&rsquo;est ? \u00c0 quoi \u00e7\u00e0 sert ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"202007\" class=\"elementor elementor-202007\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-212fbe4 e-flex e-con-boxed e-con\" data-id=\"212fbe4\" 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-1e7da00 elementor-widget elementor-widget-heading\" data-id=\"1e7da00\" 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><h2 class=\"elementor-heading-title elementor-size-default\">Gr\u00e2ce au d\u00e9veloppement du Big data et de l\u2019Intelligence artificielle, les Data Scientists peuvent utiliser des mod\u00e8les d\u2019apprentissage de donn\u00e9es pour r\u00e9aliser des analyses pr\u00e9dictives. Parmi les m\u00e9thodes les plus efficaces, il y a l\u2019ensembling.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5daf0a1 elementor-widget elementor-widget-text-editor\" data-id=\"5daf0a1\" 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<p><span style=\"font-weight: 400;\">Alors de quoi s\u2019agit-il ? Comment fonctionne l\u2019assemblage de mod\u00e8les ? Quels sont ses avantages et inconv\u00e9nients ? C\u2019est ce que nous allons voir dans cet article. <\/span><\/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-cee031e e-flex e-con-boxed e-con\" data-id=\"cee031e\" 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-868d42e elementor-widget elementor-widget-heading\" data-id=\"868d42e\" 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\">Qu\u2019est-ce que l\u2019ensembling ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e96ee60 elementor-widget elementor-widget-text-editor\" data-id=\"e96ee60\" 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><span style=\"font-weight: 400;\">L&rsquo;ensembling<\/span><b> combine plusieurs mod\u00e8les de r\u00e9seaux neuronaux<\/b><span style=\"font-weight: 400;\"> afin de r\u00e9soudre une probl\u00e9matique donn\u00e9e. Chaque mod\u00e8le utilise alors ses propres <a href=\"https:\/\/datascientest.com\/apprentissage-automatique\">techniques d\u2019apprentissage<\/a>.<\/span><span style=\"font-weight: 400;\">\u00a0Ce qui permet d\u2019enrichir les pr\u00e9dictions.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Avec l\u2019apprentissage d\u2019ensemble de mod\u00e8les, il est possible d\u2019<\/span><b>obtenir des r\u00e9sultats beaucoup plus fiables <\/b><span style=\"font-weight: 400;\">et plus pertinents qu\u2019avec tout autre mod\u00e8le unique.<\/span><\/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-27aae38 e-flex e-con-boxed e-con\" data-id=\"27aae38\" 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-02bc7d8 elementor-widget elementor-widget-heading\" data-id=\"02bc7d8\" 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\">Comment fonctionne l\u2019assemblage de mod\u00e8les ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-465fca1 elementor-widget elementor-widget-text-editor\" data-id=\"465fca1\" 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><span style=\"font-weight: 400;\">Tout mod\u00e8le d\u2019apprentissage n\u00e9cessite d<\/span><b>\u2019utiliser de grandes quantit\u00e9s de donn\u00e9es<\/b><span style=\"font-weight: 400;\"> pour aboutir \u00e0 des r\u00e9sultats pertinents. Mais en fonction de la technique employ\u00e9e, il convient de s\u00e9lectionner avec attention les bons composants (part de biais et de variance).\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Voici quelques exemples de techniques d\u2019apprentissage :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>L&rsquo;ensachage <\/b><span style=\"font-weight: 400;\">: il s\u2019agit de r\u00e9duire la variance, afin d\u2019am\u00e9liorer la stabilit\u00e9 des pr\u00e9dictions. Dans ce cas, il faut des composants pr\u00e9sentant un faible biais et une variance \u00e9lev\u00e9e. Les biais viennent alors contrecarrer les variances. Ce qui rend les r\u00e9sultats moins sensibles aux sp\u00e9cificit\u00e9s des donn\u00e9es de formation.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Le boosting<\/b><span style=\"font-weight: 400;\"> : \u00e0 l\u2019inverse, l\u2019objectif est de r\u00e9duire les biais. Pour cela, les mod\u00e8les sont utilis\u00e9s de mani\u00e8re s\u00e9quentielle.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>L&#8217;empilage <\/b><span>: il am\u00e9liore la pr\u00e9cision tout en maintenant la variance et le biais \u00e0 un niveau bas. C\u2019est particuli\u00e8rement utile pour les for\u00eats al\u00e9atoires.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81e965e elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"81e965e\" 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\/formation-machine-learning-engineer\">\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\">Apprendre \u00e0 concevoir des mod\u00e8les<\/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<div class=\"elementor-element elementor-element-7554188 elementor-widget elementor-widget-text-editor\" data-id=\"7554188\" 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><span style=\"font-weight: 400;\">Par principe, l\u2019ensembling <\/span><b>ex\u00e9cute les mod\u00e8les s\u00e9par\u00e9ment<\/b><span style=\"font-weight: 400;\"> avant de combiner les pr\u00e9dictions entre elles. Cet assemblage de mod\u00e8les permet ensuite de r\u00e9duire la variance des pr\u00e9dictions obtenues sur les donn\u00e9es de test et de validations.<\/span><\/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-ac00428 elementor-widget elementor-widget-text-editor\" data-id=\"ac00428\" 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><span style=\"text-decoration: underline;\">Bon \u00e0 savoir <\/span><\/strong><span style=\"font-weight: 400;\">: si la combinaison de plusieurs mod\u00e8les permet d\u2019obtenir des pr\u00e9dictions plus fiables, il convient toutefois d\u2019en limiter le nombre. \u00c0 la fois en raison des co\u00fbts li\u00e9s \u00e0 l&rsquo;entra\u00eenement, mais aussi \u00e0 cause d\u2019une baisse des performances li\u00e9es \u00e0 l\u2019ajout de nouveaux mod\u00e8les. G\u00e9n\u00e9ralement, le juste \u00e9quilibre se situe entre 3 et 10 mod\u00e8les form\u00e9s. Ce nombre permet alors d\u2019obtenir les meilleurs r\u00e9sultats.<\/span><\/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-3538ab7 elementor-widget elementor-widget-image\" data-id=\"3538ab7\" 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\t\t\t<img decoding=\"async\" width=\"900\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/05\/ensembling-datascientest-1.png\" class=\"attachment-full size-full wp-image-202035\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/05\/ensembling-datascientest-1.png 900w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/05\/ensembling-datascientest-1-300x167.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/05\/ensembling-datascientest-1-768x427.png 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0674d38 e-flex e-con-boxed e-con\" data-id=\"0674d38\" 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-892f6f7 elementor-widget elementor-widget-heading\" data-id=\"892f6f7\" 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\">Quels sont les avantages ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c506e93 elementor-widget elementor-widget-text-editor\" data-id=\"c506e93\" 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><span style=\"font-weight: 400;\">L\u2019ensembling est l\u2019une des techniques de Machine Learning les plus couramment utilis\u00e9es. Et pour cause, il offre aux experts data une multitude de b\u00e9n\u00e9fices :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>La pr\u00e9cision<\/b><span style=\"font-weight: 400;\"> : de mani\u00e8re g\u00e9n\u00e9rale, les assemblages de mod\u00e8les offrent de meilleurs r\u00e9sultats que n&rsquo;importe quel mod\u00e8le \u00e0 composante unique. La plupart du temps, les r\u00e9sultats s\u2019am\u00e9liorent en fonction de la taille de l\u2019ensemble. Mais comme vu pr\u00e9c\u00e9demment, il convient de trouver le juste \u00e9quilibre. C\u2019est-\u00e0-dire, ne pas ajouter trop de mod\u00e8les.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>La pertinence <\/b><span style=\"font-weight: 400;\">: les assemblages de mod\u00e8les sont plus \u00e0 m\u00eame de faire des g\u00e9n\u00e9ralisations pour r\u00e9pondre \u00e0 la probl\u00e9matique donn\u00e9e. Ce qui limite la dispersion des analyses.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>La flexibilit\u00e9 <\/b><span>: avec l\u2019ensembling, les Data Scientists ont acc\u00e8s \u00e0 de grandes<\/span><a href=\"https:\/\/datascientest.com\/la-data-le-nouvel-or-du-21eme-siecle\"><span>quantit\u00e9s de donn\u00e9es<\/span><\/a>.<span>Mais il est tout \u00e0 fait possible d\u2019adapter les r\u00e9sultats au niveau d\u2019information disponible. Notamment de s\u00e9lectionner le meilleur mod\u00e8le parmi plusieurs choix.<\/span><\/li><\/ul>\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-c39e7fc e-flex e-con-boxed e-con\" data-id=\"c39e7fc\" 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-7fb04ee elementor-widget elementor-widget-heading\" data-id=\"7fb04ee\" 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\">Quelles sont les limites ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4482664 elementor-widget elementor-widget-text-editor\" data-id=\"4482664\" 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><span style=\"font-weight: 400;\">Bien que l\u2019ensembling soit tr\u00e8s efficace, il pr\u00e9sente toutefois certaines limites. \u00c0 savoir :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Seuls les mod\u00e8les form\u00e9s avec les m\u00eames param\u00e8tres peuvent \u00eatre combin\u00e9s ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Certains mod\u00e8les d\u2019apprentissage ne peuvent \u00eatre assembl\u00e9s. C\u2019est notamment le cas de ceux form\u00e9s \u00e0 l&rsquo;aide du test crois\u00e9 K-Fold, avec des vecteurs ou des caract\u00e9ristiques d&rsquo;image, des mod\u00e8les partitionn\u00e9s ou encore des mod\u00e8les de vision par ordinateur.<\/span><\/li><\/ul>\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-d7d634f e-flex e-con-boxed e-con\" data-id=\"d7d634f\" 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-d49c550 elementor-widget elementor-widget-heading\" data-id=\"d49c550\" 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\">Ce qu\u2019il faut retenir :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1329f4b elementor-widget elementor-widget-text-editor\" data-id=\"1329f4b\" 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<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L\u2019ensembling combine plusieurs mod\u00e8les de r\u00e9seaux neuronaux pour optimiser la pertinence des r\u00e9sultats pr\u00e9dictifs.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Avant l\u2019assemblage, chaque mod\u00e8le est ex\u00e9cut\u00e9 s\u00e9par\u00e9ment, avec ses propres techniques d\u2019apprentissage.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L\u2019apprentissage d\u2019ensemble de mod\u00e8les am\u00e9liore la pr\u00e9cision et la pertinence des pr\u00e9dictions.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cela dit, il n\u2019est pas possible d\u2019utiliser cette technique avec tous les mod\u00e8les. <\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f52d7d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3f52d7d\" 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\/formation-machine-learning-engineer\">\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\">D\u00e9marrer une formation en Machine Learning<\/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<div class=\"elementor-element elementor-element-2b3ac63 e-flex e-con-boxed e-con\" data-id=\"2b3ac63\" 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-748c628 elementor-widget elementor-widget-html\" data-id=\"748c628\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu\u2019est-ce que l\u2019ensembling ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"L'ensembling combine plusieurs mod\u00e8les de r\u00e9seaux neuronaux afin de r\u00e9soudre une probl\u00e9matique donn\u00e9e. Chaque mod\u00e8le utilise alors ses propres techniques d\u2019apprentissage. Ce qui permet d\u2019enrichir les pr\u00e9dictions. Avec l\u2019apprentissage d\u2019ensemble de mod\u00e8les, il est possible d\u2019obtenir des r\u00e9sultats beaucoup plus fiables et plus pertinents qu\u2019avec tout autre mod\u00e8le unique.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Comment fonctionne l\u2019assemblage de mod\u00e8les ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Tout mod\u00e8le d\u2019apprentissage n\u00e9cessite d\u2019utiliser de grandes quantit\u00e9s de donn\u00e9es pour aboutir \u00e0 des r\u00e9sultats pertinents. Mais en fonction de la technique employ\u00e9e, il convient de s\u00e9lectionner avec attention les bons composants (part de biais et de variance).\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quels sont les avantages ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"La pr\u00e9cision : de mani\u00e8re g\u00e9n\u00e9rale, les assemblages de mod\u00e8les offrent de meilleurs r\u00e9sultats que n'importe quel mod\u00e8le \u00e0 composante unique. La plupart du temps, les r\u00e9sultats s\u2019am\u00e9liorent en fonction de la taille de l\u2019ensemble. Mais comme vu pr\u00e9c\u00e9demment, il convient de trouver le juste \u00e9quilibre. C\u2019est-\u00e0-dire, ne pas ajouter trop de mod\u00e8les.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\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>Gr\u00e2ce au d\u00e9veloppement du Big data et de l\u2019Intelligence artificielle, les Data Scientists peuvent utiliser des mod\u00e8les d\u2019apprentissage de donn\u00e9es pour r\u00e9aliser des analyses pr\u00e9dictives. Parmi les m\u00e9thodes les plus efficaces, il y a l\u2019ensembling. Alors de quoi s\u2019agit-il ? Comment fonctionne l\u2019assemblage de mod\u00e8les ? Quels sont ses avantages et inconv\u00e9nients ? C\u2019est ce [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":213199,"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>Ensembling : Qu&#039;est-ce que c&#039;est ? \u00c0 quoi \u00e7\u00e0 sert ?<\/title>\n<meta name=\"description\" content=\"Pour r\u00e9aliser des analyses pr\u00e9dictives fiables et pertinentes, les Data Scientists peuvent utiliser l\u2019ensembling (ou assemblage de mod\u00e8les).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/ensembling-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ensembling : Qu&#039;est-ce que c&#039;est ? \u00c0 quoi \u00e7\u00e0 sert ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/ensembling-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-04T07:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T13:41:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/08\/Illu_BLOG__ensembling.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"563\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/ensembling-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/ensembling-tout-savoir\",\"name\":\"Ensembling : Qu'est-ce que c'est ? \u00c0 quoi \u00e7\u00e0 sert ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2023-10-04T07:30:00+00:00\",\"dateModified\":\"2023-11-09T13:41:46+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Pour r\u00e9aliser des analyses pr\u00e9dictives fiables et pertinentes, les Data Scientists peuvent utiliser l\u2019ensembling (ou assemblage de mod\u00e8les).\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/ensembling-tout-savoir\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\",\"name\":\"Raphael Kassel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"caption\":\"Raphael Kassel\"},\"description\":\"Raphael Kassel est le Head of Data Programs chez DataScientest. 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