{"id":257719,"date":"2024-12-11T06:30:00","date_gmt":"2024-12-11T06:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=257719"},"modified":"2025-01-09T06:45:05","modified_gmt":"2025-01-09T06:45:05","slug":"bagging-vs-boosting-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/bagging-vs-boosting-tout-savoir","title":{"rendered":"Bagging vs Boosting : Tout ce qu&rsquo;il  faut savoir"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"257719\" class=\"elementor elementor-257719\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dabbc28 e-flex e-con-boxed e-con\" data-id=\"dabbc28\" 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-35730df elementor-widget elementor-widget-text-editor\" data-id=\"35730df\" 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><strong>Lorsque l\u2019apprentissage d\u2019un mod\u00e8le singulier peine \u00e0 livrer de bonnes pr\u00e9dictions, les m\u00e9thodes d\u2019apprentissage d\u2019ensemble apparaissent souvent comme une solution de choix. Les techniques d\u2019ensemble les plus connues, le Bagging (Bootstrap Aggregating) et le Boosting, ont toutes toutes deux comme objectif d\u2019am\u00e9liorer la pr\u00e9cision des pr\u00e9dictions faites lors de l\u2019apprentissage automatique en combinant les r\u00e9sultats de mod\u00e8les individuels, afin d\u2019en extraire des pr\u00e9dictions finales plus robustes et pr\u00e9cises<\/strong><span style=\"font-weight: 400;\"><strong>.<\/strong> <\/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-d6a5d78 e-flex e-con-boxed e-con\" data-id=\"d6a5d78\" 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-05c17c6 elementor-widget elementor-widget-heading\" data-id=\"05c17c6\" 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\">Bagging : La Puissance de l'Apprentissage Parall\u00e8le<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-592aa5f elementor-widget elementor-widget-image\" data-id=\"592aa5f\" 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=\"1920\" height=\"1080\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1.webp\" class=\"attachment-full size-full wp-image-257722\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1.webp 1920w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1-300x169.webp 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1-1024x576.webp 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1-768x432.webp 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1-1536x864.webp 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\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 class=\"elementor-element elementor-element-e4ca327 elementor-widget elementor-widget-text-editor\" data-id=\"e4ca327\" 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;\"><a href=\"https:\/\/datascientest.com\/machine-learning-le-bagging\">Le Bagging, introduit par Leo Breiman en 1994<\/a>, se base sur l\u2019entra\u00eenement de plusieurs versions d\u2019un pr\u00e9dicteur tel qu\u2019un arbre de d\u00e9cision, entra\u00een\u00e9 en <\/span><b>parall\u00e8le<\/b><span style=\"font-weight: 400;\"> de mani\u00e8re <\/span><b>ind\u00e9pendante<\/b><span style=\"font-weight: 400;\">. La premi\u00e8re \u00e9tape du bagging consiste \u00e0 effectuer un <\/span><b>tirage d\u2019\u00e9chantillons al\u00e9atoires avec remise<\/b><span style=\"font-weight: 400;\"> (appel\u00e9 le bootstrapping) \u00e0 partir de l\u2019ensemble des donn\u00e9es d\u2019entra\u00eenement. Chaque pr\u00e9dicteur se voit \u00eatre assign\u00e9 un \u00e9chantillon d\u2019entra\u00eenement sur lequel il \u00e9met des pr\u00e9dictions. Ces derni\u00e8res sont ensuite combin\u00e9es avec celles de tous les autres pr\u00e9dicteurs distincts. Cette derni\u00e8re \u00e9tape passe par le calcul de la moyenne des pr\u00e9dictions faites par les diff\u00e9rents mod\u00e8les (pour les pr\u00e9dictions quantitatives) ou par une m\u00e9thode de vote (pour les pr\u00e9dictions cat\u00e9gorielles), <\/span><b>o\u00f9 la pr\u00e9diction majoritaire en termes de nombre d&rsquo;occurrence ou de probabilit\u00e9 est retenue<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">La force principale du bagging r\u00e9side dans sa capacit\u00e9 \u00e0 <\/span><b>r\u00e9duire la variance sans augmenter le biais<\/b><span style=\"font-weight: 400;\">. En entra\u00eenant des mod\u00e8les sur diff\u00e9rents sous-ensembles ayant un certain pourcentage de donn\u00e9es en commun, chaque mod\u00e8le capture la diversit\u00e9 pr\u00e9sente dans les jeux de donn\u00e9es al\u00e9atoires, tout en obtenant des r\u00e9sultats finaux g\u00e9n\u00e9ralisant bien sur le jeu de donn\u00e9es test. Une analogie avec le monde r\u00e9el est la suivante : demandez l&rsquo;avis de plusieurs experts sur un probl\u00e8me complexe. Chaque expert, bien que comp\u00e9tent, peut avoir des exp\u00e9riences et des perspectives l\u00e9g\u00e8rement diff\u00e9rentes. Moyenner leurs opinions conduit souvent \u00e0 de <strong>meilleures d\u00e9cisions<\/strong> que de se fier \u00e0 un seul expert.<\/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-66f2434 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"66f2434\" 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-intelligence-artificielle\">\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\">Approfondir sur le Bagging 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<div class=\"elementor-element elementor-element-e7bff9f elementor-widget elementor-widget-text-editor\" data-id=\"e7bff9f\" 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;\">En somme,<\/span><b> l&rsquo;agr\u00e9gation de plusieurs mod\u00e8les \u00e0 forte variance<\/b><span style=\"font-weight: 400;\"> est utilis\u00e9e afin de capturer au mieux les variations pr\u00e9sentes dans chaque jeu d&rsquo;entra\u00eenement. Cette approche permet donc de <\/span><b>lisser les erreurs de pr\u00e9dictions individuelles<\/b><span style=\"font-weight: 400;\"> \u00e9mises par les diff\u00e9rents mod\u00e8les, afin de construire un mod\u00e8le global \u00e0<\/span><b> faible variance<\/b><span style=\"font-weight: 400;\"> en combinant <a href=\"https:\/\/datascientest.com\/overfitting-tout-savoir\">les pr\u00e9dictions de plusieurs mod\u00e8les \u00e0 hautes variances (overfitting)<\/a>. Le Bagging a notamment \u00e9t\u00e9 popularis\u00e9 \u00e0 travers <a href=\"https:\/\/datascientest.com\/random-forest-definition\">les Random Forests (for\u00eats al\u00e9atoires)<\/a> qui sont le r\u00e9sultat de l\u2019entra\u00eenement parall\u00e8le d\u2019arbres de d\u00e9cisions, un type de mod\u00e8le connu pour sa forte variance.<\/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-3d47ddf e-flex e-con-boxed e-con\" data-id=\"3d47ddf\" 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-e437080 elementor-widget elementor-widget-heading\" data-id=\"e437080\" 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\">Boosting : Apprentissage S\u00e9quentiel pour la R\u00e9duction des Erreurs<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3df02d8 elementor-widget elementor-widget-text-editor\" data-id=\"3df02d8\" 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;\">Contrairement au<\/span><b> Bagging<\/b><span style=\"font-weight: 400;\">, le <strong>boosting<\/strong> suit une m\u00e9thode <strong>s\u00e9quentielle<\/strong> dans la construction du mod\u00e8le final. Les pr\u00e9dicteurs individuels sont dits <\/span><b>faibles<\/b><span style=\"font-weight: 400;\"> (underfitting) et sont construits en <\/span><b>s\u00e9rie<\/b><span style=\"font-weight: 400;\">, l\u2019un \u00e0 la suite de l\u2019autre. De ce fait, chaque mod\u00e8le vise \u00e0 corriger les erreurs de celui qui le pr\u00e9c\u00e8de dans le but de diminuer le biais introduit par chaque mod\u00e8le faible. Les algorithmes de boosting incluent notamment <\/span><a href=\"https:\/\/datascientest.com\/algorithmes-de-boosting-adaboost-gradient-boosting-xgboost\">AdaBoost<span style=\"font-weight: 400;\"> (Adaptive Boosting), le <\/span>Gradient Boosting<span style=\"font-weight: 400;\"> et ses variantes <\/span>XGBoost<span style=\"font-weight: 400;\">, <\/span>LightGBM<\/a><span style=\"font-weight: 400;\">.<\/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-9a0809b elementor-widget elementor-widget-image\" data-id=\"9a0809b\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-4.webp\" class=\"attachment-full size-full wp-image-257750\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-4.webp 1200w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-4-300x172.webp 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-4-1024x585.webp 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-4-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\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 class=\"elementor-element elementor-element-307ac43 elementor-widget elementor-widget-text-editor\" data-id=\"307ac43\" 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;\">Le processus commence par un apprenant faible effectuant des pr\u00e9dictions sur le jeu de donn\u00e9es d\u2019entra\u00eenement. Les<\/span><b> instances mal pr\u00e9dites<\/b><span style=\"font-weight: 400;\"> sont alors identifi\u00e9es par l\u2019algorithme de Boosting qui leur assigne des <\/span><b>poids plus \u00e9lev\u00e9s<\/b><span style=\"font-weight: 400;\">. Le mod\u00e8le suivant se concentre plus sur ces cas pr\u00e9c\u00e9demment difficiles \u00e0 identifier lors de son entra\u00eenement afin de rendre ses pr\u00e9dictions plus robustes. Le processus continue et chaque mod\u00e8le subs\u00e9quent vise \u00e0 corriger les erreurs des apprenants faibles pr\u00e9c\u00e9dents, jusqu\u2019\u00e0 ce que le dernier mod\u00e8le de la s\u00e9rie soit entra\u00een\u00e9. Tout comme pour le <\/span><b>Bagging<\/b><span style=\"font-weight: 400;\">, le nombre de mod\u00e8les \u00e0 entra\u00eener pour contribuer aux pr\u00e9dictions finales peut \u00eatre d\u00e9termin\u00e9 empiriquement en tenant compte de la complexit\u00e9, du temps d\u2019entra\u00eenement et de la pr\u00e9cision des pr\u00e9dictions finales.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Le <\/span><b>Gradient Boosting<\/b><span style=\"font-weight: 400;\"> pousse le concept de <\/span><b>Boosting<\/b><span style=\"font-weight: 400;\"> plus loin en utilisant une approche bas\u00e9e sur la minimisation des gradients pour ajuster les pr\u00e9dictions. Chaque nouveau mod\u00e8le est entra\u00een\u00e9 pour corriger <\/span><b>les r\u00e9sidus des pr\u00e9dictions pr\u00e9c\u00e9dentes<\/b><span style=\"font-weight: 400;\"> en suivant la direction du gradient de la fonction de perte, ce qui permet une optimisation plus pr\u00e9cise et efficace.<\/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-f2e1cb3 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"f2e1cb3\" 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-intelligence-artificielle\">\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\">Se former au Boosting 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-8d80b37 e-flex e-con-boxed e-con\" data-id=\"8d80b37\" 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-3dd1e65 elementor-widget elementor-widget-heading\" data-id=\"3dd1e65\" 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\">Diff\u00e9rences Cl\u00e9s et Compromis<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-24d1bb0 elementor-widget elementor-widget-heading\" data-id=\"24d1bb0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">1. Approche d'Entra\u00eenement :<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53ecbd9 elementor-widget elementor-widget-text-editor\" data-id=\"53ecbd9\" 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><span style=\"font-weight: 400;\">Bagging : Les mod\u00e8les sont entra\u00een\u00e9s<\/span><b> ind\u00e9pendamment<\/b><span style=\"font-weight: 400;\"> et en<\/span><b> parall\u00e8le<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting : Les mod\u00e8les sont entra\u00een\u00e9s <\/span><b>s\u00e9quentiellement<\/b><span style=\"font-weight: 400;\">, chacun apprenant des erreurs pr\u00e9c\u00e9dentes.<\/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-33d84cb elementor-widget elementor-widget-heading\" data-id=\"33d84cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">2. Gestion des Erreurs :<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ab616b elementor-widget elementor-widget-text-editor\" data-id=\"7ab616b\" 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><span style=\"font-weight: 400;\">Bagging : R\u00e9duit la variance par moyenne.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting : R\u00e9duit \u00e0 la fois le biais et la variance par apprentissage s\u00e9quentiel.<\/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-3ab3eda elementor-widget elementor-widget-image\" data-id=\"3ab3eda\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-2.webp\" class=\"attachment-full size-full wp-image-257745\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-2.webp 1200w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-2-300x172.webp 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-2-1024x585.webp 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-2-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\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 class=\"elementor-element elementor-element-2a989a1 elementor-widget elementor-widget-heading\" data-id=\"2a989a1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">3. Risque de Surapprentissage :<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20d502e elementor-widget elementor-widget-text-editor\" data-id=\"20d502e\" 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><span style=\"font-weight: 400;\">Bagging : G\u00e9n\u00e9ralement plus r\u00e9sistant au <\/span><b>surapprentissage<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting : Plus sensible au<\/span><b> surapprentissage<\/b><span style=\"font-weight: 400;\">, particuli\u00e8rement lorsque celui-ci cherche \u00e0 classer correctement des donn\u00e9es <\/span><b>bruit\u00e9es<\/b><span style=\"font-weight: 400;\">.<\/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-ce65a98 elementor-widget elementor-widget-heading\" data-id=\"ce65a98\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><font size=\"4\">4. Vitesse d'Entra\u00eenement :<\/font><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-137cd18 elementor-widget elementor-widget-text-editor\" data-id=\"137cd18\" 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><span style=\"font-weight: 400;\">Bagging : Plus rapide, car les mod\u00e8les peuvent \u00eatre entra\u00een\u00e9s en parall\u00e8le.<\/span><\/li><li><span style=\"font-weight: 400;\">Boosting : Plus lent, en raison de sa nature s\u00e9quentielle.<\/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-759efe0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"759efe0\" 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-intelligence-artificielle\">\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\">Ma\u00eetriser Bagging et Boosting<\/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-1f1528a e-flex e-con-boxed e-con\" data-id=\"1f1528a\" 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-dc3bbe0 elementor-widget elementor-widget-heading\" data-id=\"dc3bbe0\" 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\">Applications Pratiques<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10d1b5f elementor-widget elementor-widget-text-editor\" data-id=\"10d1b5f\" 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;\">Les deux techniques excellent dans diff\u00e9rents sc\u00e9narios. Le Bagging performe souvent bien quand :<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Les mod\u00e8les de base sont complexes (haute variance).<\/span><\/li><li><span style=\"font-weight: 400;\">Le jeu de donn\u00e9es contient beaucoup de bruit.<\/span><\/li><li><span style=\"font-weight: 400;\">Des capacit\u00e9s de traitement parall\u00e8le sont disponibles.<\/span><\/li><li><span style=\"font-weight: 400;\">L&rsquo;interpr\u00e9tabilit\u00e9 est importante.<\/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-c4c5821 elementor-widget elementor-widget-text-editor\" data-id=\"c4c5821\" 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;\">Le Boosting brille typiquement quand :<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Les mod\u00e8les de base sont simples (haut biais).<\/span><\/li><li><span style=\"font-weight: 400;\">Les donn\u00e9es sont relativement peu bruit\u00e9es.<\/span><\/li><li><span style=\"font-weight: 400;\">Atteindre une pr\u00e9cision maximale de pr\u00e9diction est crucial.<\/span><\/li><li><span style=\"font-weight: 400;\">Les ressources informatiques permettent un traitement s\u00e9quentiel.<\/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-26ad09c elementor-widget elementor-widget-image\" data-id=\"26ad09c\" 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\t\t\t<img decoding=\"async\" width=\"1200\" height=\"686\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-3.webp\" class=\"attachment-full size-full wp-image-257746\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-3.webp 1200w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-3-300x172.webp 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-3-1024x585.webp 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-3-768x439.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\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-73883dd e-flex e-con-boxed e-con\" data-id=\"73883dd\" 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-b86a8dc elementor-widget elementor-widget-heading\" data-id=\"b86a8dc\" 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\">Consid\u00e9rations pour l'Impl\u00e9mentation<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b7fa33 elementor-widget elementor-widget-text-editor\" data-id=\"3b7fa33\" 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;\">Lors de l&rsquo;impl\u00e9mentation de ces techniques, plusieurs facteurs m\u00e9ritent attention :<\/span><\/p><ol><li><span style=\"font-weight: 400;\"><strong> Taille du <a href=\"https:\/\/datascientest.com\/dataset-definition\">Dataset<\/a><\/strong> : Les grands jeux de donn\u00e9es b\u00e9n\u00e9ficient g\u00e9n\u00e9ralement plus du bagging<\/span><\/li><li><span style=\"font-weight: 400;\"><a href=\"https:\/\/datascientest.com\/computational-resources-tout-savoir\"><strong> Ressources Computationnelles<\/strong><\/a> : Le bagging peut exploiter le traitement parall\u00e8le<\/span><\/li><li><span style=\"font-weight: 400;\"><strong> R\u00e9glage des Param\u00e8tres<\/strong> : Le boosting n\u00e9cessite g\u00e9n\u00e9ralement un r\u00e9glage plus minutieux<\/span><\/li><li><span style=\"font-weight: 400;\"><strong> Interpr\u00e9tabilit\u00e9<\/strong> : Les mod\u00e8les bagg\u00e9s tendent \u00e0 \u00eatre plus interpr\u00e9tables<\/span><\/li><\/ol>\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-08b86a6 e-flex e-con-boxed e-con\" data-id=\"08b86a6\" 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-c11ec1a elementor-widget elementor-widget-heading\" data-id=\"c11ec1a\" 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\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-042c236 elementor-widget elementor-widget-text-editor\" data-id=\"042c236\" 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;\">Le<\/span><b> bagging<\/b><span style=\"font-weight: 400;\"> et le <\/span><b>boosting<\/b><span style=\"font-weight: 400;\"> sont maintenant des techniques fondamentales en apprentissage automatique. Alors que le bagging offre <\/span><b>robustesse et simplicit\u00e9<\/b><span style=\"font-weight: 400;\"> par apprentissage parall\u00e8le, le <\/span><b>boosting<\/b><span style=\"font-weight: 400;\"> fournit de puissantes capacit\u00e9s d&rsquo;<\/span><b>am\u00e9lioration s\u00e9quentielle<\/b><span style=\"font-weight: 400;\">. Comprendre leurs forces et faiblesses respectives permet aux praticiens de choisir l&rsquo;approche appropri\u00e9e \u00e0 leur cas d&rsquo;utilisation sp\u00e9cifique.<\/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-d29cffe elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d29cffe\" 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-intelligence-artificielle\">\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\u00e9couvrir nos formations en IA<\/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-706f2dc e-flex e-con-boxed e-con\" data-id=\"706f2dc\" 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-f2065dc elementor-widget elementor-widget-html\" data-id=\"f2065dc\" 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    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Qu'est-ce que le bagging en machine learning ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Le bagging (Bootstrap Aggregating) est une technique d'ensemble en machine learning qui consiste \u00e0 entra\u00eener plusieurs mod\u00e8les sur des \u00e9chantillons al\u00e9atoires (avec remplacement) d'un m\u00eame ensemble de donn\u00e9es. Les pr\u00e9dictions des mod\u00e8les sont ensuite combin\u00e9es, g\u00e9n\u00e9ralement par moyennage ou vote majoritaire, pour am\u00e9liorer la stabilit\u00e9 et r\u00e9duire la variance.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Qu'est-ce que le boosting en machine learning ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Le boosting est une technique d'ensemble qui combine plusieurs mod\u00e8les faibles en les entra\u00eenant s\u00e9quentiellement, o\u00f9 chaque mod\u00e8le corrige les erreurs des mod\u00e8les pr\u00e9c\u00e9dents. Les mod\u00e8les sont pond\u00e9r\u00e9s en fonction de leur performance, et le boosting vise \u00e0 r\u00e9duire le biais et am\u00e9liorer la pr\u00e9cision globale.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Quelle est la diff\u00e9rence principale entre bagging et boosting ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"La principale diff\u00e9rence r\u00e9side dans la mani\u00e8re dont les mod\u00e8les sont entra\u00een\u00e9s. Le bagging entra\u00eene des mod\u00e8les en parall\u00e8le sur des \u00e9chantillons al\u00e9atoires pour r\u00e9duire la variance, tandis que le boosting entra\u00eene des mod\u00e8les s\u00e9quentiellement pour r\u00e9duire le biais en corrigeant les erreurs des mod\u00e8les pr\u00e9c\u00e9dents.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Quand utiliser le bagging ou le boosting ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Le bagging est recommand\u00e9 lorsque le mod\u00e8le de base est instable et sujet \u00e0 une forte variance (comme les arbres de d\u00e9cision). Le boosting est id\u00e9al lorsque le probl\u00e8me n\u00e9cessite une r\u00e9duction du biais et que des mod\u00e8les faibles peuvent \u00eatre combin\u00e9s pour am\u00e9liorer la pr\u00e9cision.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\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>Lorsque l\u2019apprentissage d\u2019un mod\u00e8le singulier peine \u00e0 livrer de bonnes pr\u00e9dictions, les m\u00e9thodes d\u2019apprentissage d\u2019ensemble apparaissent souvent comme une solution de choix. Les techniques d\u2019ensemble les plus connues, le Bagging (Bootstrap Aggregating) et le Boosting, ont toutes toutes deux comme objectif d\u2019am\u00e9liorer la pr\u00e9cision des pr\u00e9dictions faites lors de l\u2019apprentissage automatique en combinant les r\u00e9sultats [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":257722,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497,2500],"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>Bagging vs Boosting : Tout ce qu&#039;il faut savoir<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez les diff\u00e9rences entre Bagging et Boosting, deux techniques cl\u00e9s d&#039;apprentissage d&#039;ensemble, et apprenez comment les utiliser pour am\u00e9liorer la performance de vos mod\u00e8les d&#039;IA.\" \/>\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\/bagging-vs-boosting-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bagging vs Boosting : Tout ce qu&#039;il faut savoir\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/bagging-vs-boosting-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2024-12-11T06:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-01-09T06:45:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/11\/bagging-vs-boosting-datascientest-1.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"J\u00e9r\u00e9my Robert\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"J\u00e9r\u00e9my Robert\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/bagging-vs-boosting-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/bagging-vs-boosting-tout-savoir\",\"name\":\"Bagging vs Boosting : Tout ce qu'il faut savoir\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2024-12-11T06:30:00+00:00\",\"dateModified\":\"2025-01-09T06:45:05+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\"},\"description\":\"D\u00e9couvrez les diff\u00e9rences entre Bagging et Boosting, deux techniques cl\u00e9s d'apprentissage d'ensemble, et apprenez comment les utiliser pour am\u00e9liorer la performance de vos mod\u00e8les d'IA.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/bagging-vs-boosting-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\/7af2361c48e24e79a8aa2360e6e74925\",\"name\":\"J\u00e9r\u00e9my Robert\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"caption\":\"J\u00e9r\u00e9my Robert\"},\"description\":\"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. 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