{"id":184191,"date":"2023-01-11T18:26:00","date_gmt":"2023-01-11T18:26:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=184191"},"modified":"2023-11-09T13:54:16","modified_gmt":"2023-11-09T13:54:16","slug":"reechantillonnage-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/reechantillonnage-tout-savoir","title":{"rendered":"Le r\u00e9\u00e9chantillonnage : Une m\u00e9thode d&rsquo;\u00e9quilibrage des donn\u00e9es"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"184191\" class=\"elementor elementor-184191\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d5f4389 e-flex e-con-boxed e-con\" data-id=\"d5f4389\" 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-af466e6 elementor-widget elementor-widget-heading\" data-id=\"af466e6\" 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\"><strong>Les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es sont tr\u00e8s fr\u00e9quentes en Machine Learning. Malheureusement, elles complexifient les analyses pr\u00e9dictives. Alors pour \u00e9quilibrer ces jeux de donn\u00e9es, plusieurs m\u00e9thodes ont \u00e9t\u00e9 mises en place. Et notamment le r\u00e9\u00e9chantillonnage. Focus.<\/strong><\/h2>\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-7ea8133 e-flex e-con-boxed e-con\" data-id=\"7ea8133\" 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-8adb604 elementor-widget elementor-widget-heading\" data-id=\"8adb604\" 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 g\u00e9rer les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es avec le r\u00e9\u00e9chantillonnage ? <br><\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e206a12 elementor-widget elementor-widget-text-editor\" data-id=\"e206a12\" 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>Les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es sont caract\u00e9ris\u00e9es par <strong>des \u00e9chantillons o\u00f9 une forte disparit\u00e9 est constat\u00e9e<\/strong>. Ainsi, le ratio entre les classes n\u2019est pas de 50\/50, mais plut\u00f4t de 90\/10. C\u2019est \u00e0 partir de cet instant que les data poseront des probl\u00e8mes en <a href=\"https:\/\/datascientest.com\/machine-learning-et-voyage\">Machine\u00a0Learning<\/a>\u00a0et <a href=\"https:\/\/datascientest.com\/deep-learning-avec-python-decouvrez-les-fondamentaux\">Deep Learning<\/a>.<\/p><p>Cela renvoie souvent aux \u00e9v\u00e9nements relativement rares, comme les fraudes \u00e0 l\u2019assurance ou la d\u00e9tection d\u2019une maladie. Par exemple, sur une population, il se peut qu\u2019une grande majorit\u00e9 soit en bonne sant\u00e9, et seulement 0,1 % soit atteinte de scl\u00e9rose en plaques. Les personnes saines sont alors la classe majoritaire et les personnes malades sont consid\u00e9r\u00e9es comme la classe minoritaire. Si ces \u00e9v\u00e9nements sont assez courants, ils ne repr\u00e9sentent qu\u2019une infime partie de l\u2019\u00e9chantillon \u00e9tudi\u00e9. D\u2019o\u00f9 <strong>la difficult\u00e9 \u00e0 les pr\u00e9dire<\/strong>.<\/p><p>En Machine Learning, les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es sont tr\u00e8s fr\u00e9quentes, en particulier pour la <a href=\"https:\/\/datascientest.com\/algorithme-de-classification-definition-et-principaux-modeles\">classification binaire<\/a>. C\u2019est pourquoi, plusieurs m\u00e9thodes se d\u00e9veloppent pour mieux g\u00e9rer les probl\u00e8mes de classification d\u00e9s\u00e9quilibr\u00e9e. Notamment \u00e0 travers le r\u00e9\u00e9chantillonnage des donn\u00e9es.<\/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-839901c e-flex e-con-boxed e-con\" data-id=\"839901c\" 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-78d9fb1 elementor-widget elementor-widget-heading\" data-id=\"78d9fb1\" 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 deux m\u00e9thodes de r\u00e9\u00e9chantillonnage ? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-088a144 elementor-widget elementor-widget-text-editor\" data-id=\"088a144\" 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 r\u00e9\u00e9chantillonnage consiste \u00e0 modifier l\u2019ensemble des donn\u00e9es avant d&rsquo;entra\u00eener le <a href=\"https:\/\/datascientest.com\/quest-ce-que-cest-le-predictive-modelling\">mod\u00e8le pr\u00e9dictif<\/a>. Cela permet d\u2019\u00e9quilibrer les donn\u00e9es pour faciliter la pr\u00e9diction.<\/p><p>\u00c0 cette fin, il existe deux m\u00e9thodes de r\u00e9\u00e9chantillonnage.<\/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-e47a2e7 elementor-widget elementor-widget-heading\" data-id=\"e47a2e7\" 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\">Le sur-\u00e9chantillonnage<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e3f866 elementor-widget elementor-widget-text-editor\" data-id=\"6e3f866\" 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>\u00c9galement appel\u00e9 oversampling, il s\u2019agit d\u2019<strong>augmenter le nombre de donn\u00e9es appartenant \u00e0 la classe minoritaire<\/strong>, jusqu\u2019\u00e0 atteindre un certain \u00e9quilibre. Ou du moins un taux satisfaisant pour r\u00e9aliser des pr\u00e9dictions fiables.<\/p><p>Les Data Scientists peuvent utiliser deux m\u00e9thodes de r\u00e9\u00e9chantillonnage. \u00c0 savoir :<\/p><ul><li><strong>Le sur-\u00e9chantillonnage al\u00e9atoire<\/strong> : ici, les donn\u00e9es minoritaires sont clon\u00e9es plusieurs fois de mani\u00e8re al\u00e9atoire. Cette technique est surtout utile pour les mod\u00e8les lin\u00e9aires, comme la r\u00e9gression logistique.<\/li><li><strong>Le sur-\u00e9chantillonnage synth\u00e9tique<\/strong> : l\u2019id\u00e9e est, l\u00e0 encore, d\u2019ajouter des donn\u00e9es de classes minoritaires. Mais au lieu de les copier \u00e0 l\u2019identique, l\u2019algorithme cr\u00e9e des donn\u00e9es distinctes, mais similaires.<\/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-1bf0e81 elementor-widget elementor-widget-heading\" data-id=\"1bf0e81\" 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\">Le sous-\u00e9chantillonnage<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2b9d50a elementor-widget elementor-widget-text-editor\" data-id=\"2b9d50a\" 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>\u00c0 l\u2019inverse, l\u2019undersampling va <strong>diminuer le nombre de donn\u00e9es de la classe majoritaire pour \u00e9quilibrer le ratio<\/strong>.<\/p><p>On utilise principalement le sous-\u00e9chantillonnage al\u00e9atoire. C\u2019est-\u00e0-dire que les donn\u00e9es majoritaires sont retir\u00e9es de mani\u00e8re al\u00e9atoire. Il faut privil\u00e9gier cette technique de r\u00e9\u00e9chantillonnage lorsque vous disposez de grands jeux de donn\u00e9es (au minimum plusieurs dizaines de milliers de cas).<\/p><p>Si cette m\u00e9thode est la plus courante, vous pouvez aussi utiliser l\u2019undersampling des observations frontali\u00e8res ou le clustering-based undersampling. C&rsquo;est-\u00e0-dire de retirer sp\u00e9cifiquement certaines donn\u00e9es majoritaires.<\/p><p>Dans les deux cas, il n\u2019est <strong>pas n\u00e9cessaire d\u2019obtenir un \u00e9quilibre parfait<\/strong> de 50\/50. Il est tout \u00e0 fait possible de retirer des donn\u00e9es majoritaires ou d\u2019ajouter des donn\u00e9es minoritaires afin d\u2019obtenir un ratio de 45\/55 ou encore 40\/60.<\/p><p>Par ailleurs, n\u2019h\u00e9sitez pas \u00e0 <strong>tester plusieurs ratios de classes<\/strong> afin de s\u00e9lectionner celui qui vous offrira les meilleures performances de pr\u00e9diction.<\/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-9d1646f elementor-widget elementor-widget-text-editor\" data-id=\"9d1646f\" 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 d\u00e9sirez apprendre un peu plus concernant le r\u00e9\u00e9chantillonnage de donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es, nous vous invitons \u00e0 <strong>d\u00e9couvrir nos formations en Data Science<\/strong>.<\/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-f5c1b76 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"f5c1b76\" 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\/formations-data\">\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\">Je d\u00e9couvre les formations data<\/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-dc71c94 e-flex e-con-boxed e-con\" data-id=\"dc71c94\" 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-f7ebd65 elementor-widget elementor-widget-html\" data-id=\"f7ebd65\" 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\": \"Quelles sont les deux m\u00e9thodes de r\u00e9\u00e9chantillonnage ? \u200b\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"\u00c9galement appel\u00e9 oversampling, il s\u2019agit d\u2019augmenter le nombre de donn\u00e9es appartenant \u00e0 la classe majoritaire, jusqu\u2019\u00e0 atteindre un certain \u00e9quilibre. 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Malheureusement, elles complexifient les analyses pr\u00e9dictives. Alors pour \u00e9quilibrer ces jeux de donn\u00e9es, plusieurs m\u00e9thodes ont \u00e9t\u00e9 mises en place. Et notamment le r\u00e9\u00e9chantillonnage. Focus. Comment g\u00e9rer les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es avec le r\u00e9\u00e9chantillonnage ? Les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es sont caract\u00e9ris\u00e9es par des \u00e9chantillons o\u00f9 une forte disparit\u00e9 [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":184193,"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>Le r\u00e9\u00e9chantillonnage : Une m\u00e9thode d&#039;\u00e9quilibrage des donn\u00e9es<\/title>\n<meta name=\"description\" content=\"Les donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es sont tr\u00e8s fr\u00e9quentes en Machine Learning. Malheureusement, elles complexifient les analyses pr\u00e9dictives. Alors pour \u00e9quilibrer ces jeux de donn\u00e9es, plusieurs m\u00e9thodes ont \u00e9t\u00e9 mises en place. 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