{"id":203601,"date":"2023-06-16T10:41:48","date_gmt":"2023-06-16T10:41:48","guid":{"rendered":"https:\/\/datascientest.com\/?p=203601"},"modified":"2023-11-09T13:52:02","modified_gmt":"2023-11-09T13:52:02","slug":"correlations-de-pearson-et-de-spearman","status":"publish","type":"post","link":"https:\/\/datascientest.com\/correlations-de-pearson-et-de-spearman","title":{"rendered":"Corr\u00e9lations de Pearson et de Spearman : Tout comprendre"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"203601\" class=\"elementor elementor-203601\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7c4133f e-flex e-con-boxed e-con\" data-id=\"7c4133f\" 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-ef1be39 elementor-widget elementor-widget-heading\" data-id=\"ef1be39\" 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\">La corr\u00e9lation est un outil statistique utilis\u00e9 pour le Machine Learning afin d\u2019identifier des relations de d\u00e9pendance entre plusieurs variables. Il existe plusieurs types de corr\u00e9lations. D\u00e9couvrez ci-dessous plus de d\u00e9tails sur les corr\u00e9lations de Pearson et de Spearman.<\/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-904c49d e-flex e-con-boxed e-con\" data-id=\"904c49d\" 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-8324664 elementor-widget elementor-widget-text-editor\" data-id=\"8324664\" 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;\">Pour l\u2019analyse de donn\u00e9es, un <a href=\"https:\/\/datascientest.com\/metier-data-scientist\">Data Scientist<\/a> dispose de <a href=\"https:\/\/datascientest.com\/les-outils-dans-le-quotidien-dun-data-scientist\">plusieurs outils statistiques<\/a>. Parmi ces outils, il peut utiliser la corr\u00e9lation. Il s&rsquo;agit d\u2019une mesure statistique particuli\u00e8rement utile qui permet d\u2019\u00e9tudier la relation entre deux variables \u00e0 partir du calcul d\u2019un coefficient de corr\u00e9lation. La corr\u00e9lation correspond \u00e0 la force (indiqu\u00e9e par la valeur absolue du coefficient) ainsi qu\u2019\u00e0 la direction (indiqu\u00e9e par la signe du coefficient) de <strong>la relation entre ces variables<\/strong>. La direction peut \u00eatre soit positive (lorsqu\u2019une x augmente, y augmente aussi) ou n\u00e9gative (lorsque x augmente, y diminue ou l\u2019inverse).\u00a0 Il existe plusieurs types de corr\u00e9lations. Parmi ces corr\u00e9lations, il y en a deux qui sont particuli\u00e8rement utilis\u00e9es : la corr\u00e9lation de Pearson et la corr\u00e9lation de Spearman. Ces deux types de corr\u00e9lations vont \u00eatre d\u00e9taill\u00e9s dans la suite de 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-b408700 e-flex e-con-boxed e-con\" data-id=\"b408700\" 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-d37e1e2 elementor-widget elementor-widget-heading\" data-id=\"d37e1e2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">La corr\u00e9lation de Pearson<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7733d90 elementor-widget elementor-widget-text-editor\" data-id=\"7733d90\" 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;\">La <strong>corr\u00e9lation de Pearson<\/strong>, aussi appel\u00e9e corr\u00e9lation lin\u00e9aire, permet de <strong>mesurer la relation lin\u00e9aire entre deux variables continues<\/strong>. La corr\u00e9lation de Pearson est indiqu\u00e9e par la valeur du coefficient de corr\u00e9lation r calcul\u00e9e \u00e0 l\u2019aide de la formule suivante :<\/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-44ff8df elementor-widget elementor-widget-image\" data-id=\"44ff8df\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"655\" height=\"337\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image5-2.png\" class=\"attachment-full size-full wp-image-203611\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image5-2.png 655w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image5-2-300x154.png 300w\" sizes=\"(max-width: 655px) 100vw, 655px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Formule du coefficient de Pearson<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae9b9ef elementor-widget elementor-widget-text-editor\" data-id=\"ae9b9ef\" 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;\">Avant de calculer le coefficient de Pearson, il faut s\u2019assurer que les donn\u00e9es v\u00e9rifient les hypoth\u00e8ses suivantes :\u00a0<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L\u2019\u00e9chantillon de donn\u00e9es est al\u00e9atoire (repr\u00e9sentatif de la population)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les variables sont quantitatives (continues)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les donn\u00e9es sont associ\u00e9es par paires (on associe \u00e0 chaque valeur x, une valeur y)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les observations sont ind\u00e9pendantes<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les donn\u00e9es sont distribu\u00e9es normalement<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Il existe une relation lin\u00e9aire entre les variables<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Aucun outlier n\u2019est pr\u00e9sent dans les donn\u00e9es<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92a5175 elementor-widget elementor-widget-text-editor\" data-id=\"92a5175\" 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;\">La valeur du coefficient de corr\u00e9lation r est comprise entre -1 et 1. Il y a plusieurs cas possibles selon la valeur de r :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si r est proche de 1, alors les variables sont d\u00e9pendantes lin\u00e9airement positivement.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si r est proche de 0, alors il n\u2019y a aucune relation lin\u00e9aire entre les\u00a0 variables<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si r est proche de -1, alors les variables sont d\u00e9pendantes lin\u00e9airement n\u00e9gativement.<\/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-023c77c elementor-widget elementor-widget-image\" data-id=\"023c77c\" 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=\"450\" height=\"112\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-2.png\" class=\"attachment-full size-full wp-image-203615\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-2.png 450w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-2-300x75.png 300w\" sizes=\"(max-width: 450px) 100vw, 450px\" \/>\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-2e4060d elementor-widget elementor-widget-image\" data-id=\"2e4060d\" 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=\"960\" height=\"603\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-3.png\" class=\"attachment-full size-full wp-image-203616\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-3.png 960w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-3-300x188.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-3-768x482.png 768w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>\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-e5049fe elementor-widget elementor-widget-text-editor\" data-id=\"e5049fe\" 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;\">Un exemple d\u2019application de la corr\u00e9lation de Pearson serait par exemple l\u2019\u00e9tude de la relation entre la consommation de viande et l\u2019esp\u00e9rance de vie selon le pays.<\/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-aa4e307 e-flex e-con-boxed e-con\" data-id=\"aa4e307\" 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-ca435c5 elementor-widget elementor-widget-heading\" data-id=\"ca435c5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">La corr\u00e9lation de Spearman<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29f52ca elementor-widget elementor-widget-text-editor\" data-id=\"29f52ca\" 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;\">La <strong>corr\u00e9lation de Spearman<\/strong> est une mesure de corr\u00e9lation qui <strong>mesure une relation de monotonie entre deux variables \u00e0 partir du rang des donn\u00e9es<\/strong>. Un exemple de d\u00e9termination du rang des donn\u00e9es est : [58,70,40] devient\u00a0 [2,1,3]. On utilise souvent la corr\u00e9lation de Spearman pour des donn\u00e9es constitu\u00e9es d\u2019outliers. Afin de mesurer la corr\u00e9lation de Spearman, l\u2019indicateur utilis\u00e9 est le coefficient de Spearman rs aussi appel\u00e9 coefficient de rang indiqu\u00e9e par la formule ci-dessous. Dans cette formule, la variable n indique le nombre de points de la s\u00e9rie de donn\u00e9es. La variable d correspond au carr\u00e9 de la diff\u00e9rence des rangs entre chaque point de coordonn\u00e9es (x,y).<\/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-427090e elementor-widget elementor-widget-image\" data-id=\"427090e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"297\" height=\"92\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image1-4.png\" class=\"attachment-full size-full wp-image-203617\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Formule du coefficient de Spearman<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7429007 elementor-widget elementor-widget-text-editor\" data-id=\"7429007\" 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;\">Avant de calculer le coefficient de Spearman, il faut s\u2019assurer que les donn\u00e9es v\u00e9rifient les hypoth\u00e8ses suivantes :\u00a0<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L\u2019\u00e9chantillon de donn\u00e9es est al\u00e9atoire\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">La relation entre les variables est monotone\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les donn\u00e9es sont associ\u00e9es par paires<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les observations sont ind\u00e9pendantes<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Il existe une relation de monotonie entre les variables<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les variables sont ordinales ou continues.<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e352ae elementor-widget elementor-widget-text-editor\" data-id=\"5e352ae\" 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\u2019interpr\u00e9tation du coefficient de Spearman rs varie selon les valeurs obtenues :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si rs est proche de 1, alors il y a une relation monotone positive entre les variables.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si rs est proche de 0 alors il n\u2019y a aucune relation monotone entre les variables<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Si rs est proche de -1 alors, il y a une relation monotone n\u00e9gative entre les variables.<\/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-9c883df elementor-widget elementor-widget-text-editor\" data-id=\"9c883df\" 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;\">Un exemple d\u2019application de la corr\u00e9lation de Spearman serait l\u2019\u00e9tude de la relation entre les pr\u00e9f\u00e9rences d\u2019un consommateur et le prix du produit.<\/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-d8d6bd7 elementor-widget elementor-widget-image\" data-id=\"d8d6bd7\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"787\" height=\"636\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/Fichier-148.png\" class=\"attachment-full size-full wp-image-203624\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/Fichier-148.png 787w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/Fichier-148-300x242.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/Fichier-148-768x621.png 768w\" sizes=\"(max-width: 787px) 100vw, 787px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">sans le \"?\"<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-24b155b e-flex e-con-boxed e-con\" data-id=\"24b155b\" 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-fcf7b5c elementor-widget elementor-widget-heading\" data-id=\"fcf7b5c\" 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-55113ce elementor-widget elementor-widget-text-editor\" data-id=\"55113ce\" 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 corr\u00e9lations de Spearman et de Pearson sont <strong>deux mesures de corr\u00e9lation diff\u00e9rentes qui s\u2019appliquent dans des situations sp\u00e9cifiques<\/strong>. La corr\u00e9lation de Spearman utilise le rang des donn\u00e9es pour mesurer la monotonie entre des variables ordinales ou continues. La corr\u00e9lation de Pearson quant \u00e0 elle d\u00e9tecte des relations lin\u00e9aires entre des variables quantitatives avec des donn\u00e9es suivant une distribution normale. Dans le cas d\u2019un probl\u00e8me de <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\">Machine Learning<\/a>, il est souvent question d\u2019utiliser des matrices de corr\u00e9lation constitu\u00e9es des coefficients de corr\u00e9lations entre l\u2019ensemble des variables d\u2019un jeu de donn\u00e9es. La notion de corr\u00e9lation est donc importante pour le Machine Learning.<\/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-f40b287 elementor-widget elementor-widget-text-editor\" data-id=\"f40b287\" 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;\">Si vous souhaitez vous former au Machine Learning pour aller plus en profondeur sur le domaine de la data science, n\u2019h\u00e9sitez pas \u00e0 consulter nos formations de <a href=\"https:\/\/datascientest.com\/formation-data-scientist\">Data Scientist<\/a> et <a href=\"https:\/\/datascientest.com\/formation-data-analyst\">Data Analyst<\/a>.<\/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-957767c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"957767c\" 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-data-scientist\">\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 de Data Scientist<\/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-f8a3c02 e-flex e-con-boxed e-con\" data-id=\"f8a3c02\" 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-461694e elementor-widget elementor-widget-html\" data-id=\"461694e\" 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\": \"La corr\u00e9lation de Pearson\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"La corr\u00e9lation de Pearson, aussi appel\u00e9e corr\u00e9lation lin\u00e9aire, permet de mesurer la relation lin\u00e9aire entre deux variables continues. La corr\u00e9lation de Pearson est indiqu\u00e9e par la valeur du coefficient de corr\u00e9lation r calcul\u00e9e \u00e0 l\u2019aide de la formule suivante :\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"La corr\u00e9lation de Spearman\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"La corr\u00e9lation de Spearman est une mesure de corr\u00e9lation qui mesure une relation de monotonie entre deux variables \u00e0 partir du rang des donn\u00e9es. Un exemple de d\u00e9termination du rang des donn\u00e9es est : [58,70,40] devient  [2,1,3]. On utilise souvent la corr\u00e9lation de Spearman pour des donn\u00e9es constitu\u00e9es d\u2019outliers. Afin de mesurer la corr\u00e9lation de Spearman, l\u2019indicateur utilis\u00e9 est le coefficient de Spearman rs aussi appel\u00e9 coefficient de rang indiqu\u00e9e par la formule ci-dessous. Dans cette formule, la variable n indique le nombre de points de la s\u00e9rie de donn\u00e9es. La variable d correspond au carr\u00e9 de la diff\u00e9rence des rangs entre chaque point de coordonn\u00e9es (x,y).\"\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>La corr\u00e9lation est un outil statistique utilis\u00e9 pour le Machine Learning afin d\u2019identifier des relations de d\u00e9pendance entre plusieurs variables. Il existe plusieurs types de corr\u00e9lations. D\u00e9couvrez ci-dessous plus de d\u00e9tails sur les corr\u00e9lations de Pearson et de Spearman. Pour l\u2019analyse de donn\u00e9es, un Data Scientist dispose de plusieurs outils statistiques. Parmi ces outils, il [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":203603,"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>Corr\u00e9lations de Pearson et de Spearman : Tout comprendre<\/title>\n<meta name=\"description\" content=\"La corr\u00e9lation est un outil statistique utilis\u00e9 pour le Machine Learning. 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