{"id":590,"date":"2019-12-11T21:00:00","date_gmt":"2019-12-11T21:00:00","guid":{"rendered":"http:\/\/52.49.6.100\/?p=590"},"modified":"2023-11-09T14:15:06","modified_gmt":"2023-11-09T14:15:06","slug":"xgboost-grand-gagnant-des-competitions-machine-learning-algorithme","status":"publish","type":"post","link":"https:\/\/datascientest.com\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme","title":{"rendered":"XGBoost, le grand gagnant des comp\u00e9titions"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"590\" class=\"elementor elementor-590\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3ea9b63 e-flex e-con-boxed e-con\" data-id=\"3ea9b63\" 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-293e23c elementor-widget elementor-widget-text-editor\" data-id=\"293e23c\" 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>XGBoost signifie eXtreme Gradient Boosting. Comme son nom l\u2019indique, c\u2019est un algorithme de Gradient Boosting. Il est cod\u00e9 en C++ et disponible dans \u00e0 peu pr\u00e8s tous les langages de programmations utiles en Machine Learning, tels que Python, R ou encore Julia.<\/h2>\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-19be6e1 e-flex e-con-boxed e-con\" data-id=\"19be6e1\" 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-7914590 elementor-widget elementor-widget-heading\" data-id=\"7914590\" 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\">Qu\u2019est-ce que le Gradient Boosting ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4136aaf elementor-widget elementor-widget-text-editor\" data-id=\"4136aaf\" 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>Le<strong> Gradient Boosting<\/strong> est un algorithme particulier de Boosting.\u00a0<\/p><blockquote><p>Le Boosting consiste \u00e0 assembler plusieurs <strong>\u00ab\u00a0weak learners\u00a0\u00bb<\/strong> pour en faire un <strong>\u00ab\u00a0strong learner\u00a0\u00bb<\/strong>, c\u2019est-\u00e0-dire assembler plusieurs algorithmes ayant une performance peu \u00e9lev\u00e9e pour en cr\u00e9er un beaucoup plus efficace et satisfaisant. L\u2019assemblage de <strong>\u00ab\u00a0weak learners\u00a0\u00bb<\/strong> en <strong>\u00ab\u00a0strong learner\u00a0\u00bb<\/strong> se fait par l\u2019appel successif de ceux-ci pour estimer une variable d\u2019int\u00e9r\u00eat.\u00a0<\/p><\/blockquote><p>Dans le cadre d\u2019une r\u00e9gression, le principe va \u00eatre d\u2019estimer nos outputs par le mod\u00e8le 1, puis d\u2019utiliser les r\u00e9sidus de ce mod\u00e8le comme variable cible du mod\u00e8le 2 et ainsi de suite\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-0573d6e elementor-widget elementor-widget-image\" data-id=\"0573d6e\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/plugins\/elementor\/assets\/images\/placeholder.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\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-ac06362 elementor-widget elementor-widget-text-editor\" data-id=\"ac06362\" 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\tPour pouvoir pr\u00e9dire un output en fonction d\u2019un input dont on ne connait pas la variable cible, il faut pr\u00e9dire le r\u00e9sidu de chaque mod\u00e8le et ensuite en faire la somme\u00a0:\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-00b095a elementor-widget elementor-widget-image\" data-id=\"00b095a\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/plugins\/elementor\/assets\/images\/placeholder.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\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-6f39ce1 elementor-widget elementor-widget-text-editor\" data-id=\"6f39ce1\" 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>Dans le cadre d\u2019une classification, chaque individu dispose d\u2019un poids qui sera le m\u00eame au d\u00e9part, et qui, si un mod\u00e8le se trompe, sera augment\u00e9 avant d\u2019estimer le mod\u00e8le suivant (qui prendra donc en compte ces poids)\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-887477a elementor-widget elementor-widget-image\" data-id=\"887477a\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/plugins\/elementor\/assets\/images\/placeholder.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\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-9b2220e elementor-widget elementor-widget-text-editor\" data-id=\"9b2220e\" 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>La particularit\u00e9 du Gradient Boosting est que dans la classification, l\u2019actualisation des poids se calculera de la m\u00eame fa\u00e7on que la descente de <strong>gradient stochastique<\/strong>, et dans la r\u00e9gression, la fonction de co\u00fbt globale aura aussi la m\u00eame structure que la descente de gradient stochastique.<\/p><p>Le Gradient Boosting est la plupart du temps utilis\u00e9 avec des algorithmes d\u2019<a href=\"https:\/\/datascientest.com\/random-forest-definition\">Arbre de D\u00e9cision<\/a>, consid\u00e9r\u00e9s dans ce cadre comme des \u00ab\u00a0weak learners\u00a0\u00bb.<\/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-e01b181 e-flex e-con-boxed e-con\" data-id=\"e01b181\" 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-1867531 elementor-widget elementor-widget-heading\" data-id=\"1867531\" 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\">Sp\u00e9cificit\u00e9s de XGBoost<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1de7bd4 elementor-widget elementor-widget-text-editor\" data-id=\"1de7bd4\" 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>La principale diff\u00e9rence entre XGBoost et d\u2019autres<strong> impl\u00e9mentations de la m\u00e9thode du Gradient Boosting<\/strong> r\u00e9side dans le fait que XGBoost est informatiquement optimis\u00e9 pour rendre les diff\u00e9rents calculs n\u00e9cessaires \u00e0 l\u2019application d\u2019un Gradient Boosting rapide. Plus pr\u00e9cis\u00e9ment, XGBoost traite les donn\u00e9es en <strong>plusieurs blocs compress\u00e9s<\/strong> permettant de les trier \u00a0beaucoup plus rapidement ainsi que de les traiter en parall\u00e8le.<\/p><p>Mais les avantages de XGBoost ne sont pas uniquement li\u00e9s \u00e0 l\u2019impl\u00e9mentation de l\u2019algorithme, et donc \u00e0 ses performances, mais aussi aux divers param\u00e8tres que celui-ci propose. En effet XGBoost propose <strong>un panel d\u2019hyperparam\u00e8tres tr\u00e8s important<\/strong> ; il est ainsi possible gr\u00e2ce \u00e0 cette diversit\u00e9 de param\u00e8tres, d\u2019avoir un contr\u00f4le total sur l\u2019impl\u00e9mentation du Gradient Boosting. Il est aussi possible de rajouter diff\u00e9rentes r\u00e9gularisations dans la fonction de perte, limitant un ph\u00e9nom\u00e8ne qui arrive assez souvent lors de l\u2019utilisation d\u2019algorithmes de Gradient Boosting\u00a0: <strong>l\u2019overfitting (le sur-apprentissage).<\/strong><\/p><p>C\u2019est pour cela que XGBoost est souvent l\u2019algorithme gagnant des comp\u00e9titions\u00a0<a href=\"https:\/\/datascientest.com\/kaggle-tout-ce-quil-a-savoir-sur-cette-plateforme\">Kaggle<\/a>, il est rapide, pr\u00e9cis et efficace, permettant une souplesse de man\u0153uvre in\u00e9dite sur le Gradient Boosting. Finalement, rappelons-le, le Gradient Boosting servant principalement \u00e0 am\u00e9liorer des mod\u00e8les faibles, XGBoost aura quasi tout le temps de meilleurs r\u00e9sultats que son <strong>mod\u00e8le faible de base<\/strong>.\u00a0<\/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-6ecc85a e-flex e-con-boxed e-con\" data-id=\"6ecc85a\" 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-256ef6d elementor-widget elementor-widget-text-editor\" data-id=\"256ef6d\" 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>Si vous souhaitez en savoir plus sur XGBoost, le Gradient Boosting, ou encore\u00a0<a href=\"https:\/\/datascientest.com\/formations-data\">nos modules de Machine Learning<\/a>, vous pouvez\u00a0<a href=\"https:\/\/datascientest.com\/rdv\">prendre contact avec nos \u00e9quipes<\/a>.<\/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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>XGBoost signifie eXtreme Gradient Boosting. Cod\u00e9 en C++, il est disponible dans \u00e0 peu pr\u00e8s tous les langages de programmations utiles en Machine Learning. Quels sont ses atouts ? Pourquoi remporte t-il si r\u00e9guli\u00e8rement les comp\u00e9titions Kaggle ?<\/p>\n","protected":false},"author":77,"featured_media":177156,"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>XGBoost, le grand gagnant des comp\u00e9titions - datascientest.com<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez le XGBoost : algorithme de Gradient Boosting disponible dans la majorit\u00e9 des langages de programmations utiles en Machine Learning!\" \/>\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\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"XGBoost, le grand gagnant des comp\u00e9titions\" \/>\n<meta property=\"og:description\" content=\"XGBoost signifie eXtreme Gradient Boosting. Cod\u00e9 en C++, il est disponible dans \u00e0 peu pr\u00e8s tous les langages de programmations utiles en Machine Learning. Quels sont ses atouts ? Pourquoi remporte t-il si r\u00e9guli\u00e8rement les comp\u00e9titions Kaggle ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2019-12-11T21:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:15:06+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2019\/12\/xgboost.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\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=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme\",\"url\":\"https:\/\/datascientest.com\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme\",\"name\":\"XGBoost, le grand gagnant des comp\u00e9titions - datascientest.com\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2019-12-11T21:00:00+00:00\",\"dateModified\":\"2023-11-09T14:15:06+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"D\u00e9couvrez le XGBoost : algorithme de Gradient Boosting disponible dans la majorit\u00e9 des langages de programmations utiles en Machine Learning!\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/xgboost-grand-gagnant-des-competitions-machine-learning-algorithme\"]}]},{\"@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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