{"id":592,"date":"2019-02-21T00:00:00","date_gmt":"2019-02-20T23:00:00","guid":{"rendered":"https:\/\/blog.deepki.com\/2019\/02\/21\/machine-learning-comment-ca-marche\/"},"modified":"2023-11-16T15:59:45","modified_gmt":"2023-11-16T14:59:45","slug":"machine-learning-comment-ca-marche","status":"publish","type":"post","link":"https:\/\/www.deepki.com\/fr\/blog\/machine-learning-comment-ca-marche\/","title":{"rendered":"Le machine learning, comment \u00e7a marche ?"},"content":{"rendered":"<p style=\"text-align: justify;\"><span style=\"font-weight: bold;\"><span class=\"dropcap\">T<\/span><strong>out d\u2019abord, qu\u2019est-ce que le machine learning ? Nous le d\u00e9finirons ici comme un ensemble de m\u00e9thodes qui, apr\u00e8s <a href=\"https:\/\/www.deepki.com\/fr\/blog\/data-management-quelle-strategie\/\">l\u2019analyse et l\u2019interpr\u00e9tation automatiques de donn\u00e9es<\/a>, permet \u00e0 un syst\u00e8me de r\u00e9pondre \u00e0 un probl\u00e8me pr\u00e9cis face \u00e0 un nouveau jeu de donn\u00e9es. En machine learning, on distingue deux fa\u00e7ons de poser un probl\u00e8me : l\u2019apprentissage supervis\u00e9 VS non-supervis\u00e9 et les m\u00e9thodes de classification VS r\u00e9gression. Comment utilise-t-on ces m\u00e9thodes et quelles r\u00e9ponses peuvent-elles apporter en mati\u00e8re de performance \u00e9nerg\u00e9tique ? Pr\u00e9sentation de diff\u00e9rentes approches du machine learning illustr\u00e9es par des applications concr\u00e8tes.<\/strong><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>L\u2019apprentissage supervis\u00e9 VS. non-supervis\u00e9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><strong>La grande diff\u00e9rence entre les deux m\u00e9thodes d\u2019apprentissage r\u00e9side dans le type de donn\u00e9es analys\u00e9es au d\u00e9part.<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Dans le cadre de l\u2019apprentissage supervis\u00e9<\/strong>, nous avons \u00e0 disposition des donn\u00e9es \u00e9tiquet\u00e9es. C\u2019est-\u00e0-dire qu\u2019on a associ\u00e9 \u00e0 un individu comprenant un certain nombre de caract\u00e9ristiques une cat\u00e9gorie ou une valeur chiffr\u00e9e. Cette cat\u00e9gorie ou valeur chiffr\u00e9e est d\u00e9sign\u00e9e par le terme d\u2019 \u00ab \u00e9tiquette \u00bb ou \u00ab label \u00bb.<\/span><br \/><span style=\"color: #000000;\">Par exemple, un probl\u00e8me pourrait \u00eatre de pr\u00e9dire la consommation \u00e9nerg\u00e9tique (ici, l\u2019\u00e9tiquette) d\u2019un b\u00e2timent (ici, l\u2019individu \u00e9tudi\u00e9) gr\u00e2ce \u00e0 ses donn\u00e9es de surface, nombre d\u2019employ\u00e9s, g\u00e9olocalisation etc. (ici, ses caract\u00e9ristiques).<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Dans l\u2019apprentissage non-supervis\u00e9,<\/strong> ces fameuses \u00e9tiquettes ne sont pas connues, ou pas disponibles. Tous les individus que nous \u00e9tudions se ressemblent a priori. L\u2019objectif de l\u2019apprentissage non-supervis\u00e9 est donc d\u2019organiser les donn\u00e9es pour comprendre ce qui les distingue et les rassemble.<\/span><br \/><span style=\"color: #000000;\">Par exemple, un probl\u00e8me pourrait \u00eatre de segmenter un ensemble de b\u00e2timents au sein d\u2019un parc immobilier gr\u00e2ce aux donn\u00e9es de surface, nombre d\u2019employ\u00e9s, g\u00e9olocalisation, consommation \u00e9nerg\u00e9tique etc. afin de trouver les groupes de b\u00e2timents comparables entre eux.<\/span><\/p>\n<p><span style=\"color: #000000;\">Dans cette cat\u00e9gorie de probl\u00e8me, nous pouvons citer les m\u00e9thodes de clustering (segmentation) ou de d\u00e9tection d\u2019anomalies.<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Cas d\u2019usage Paul<\/strong><\/span><br \/><span style=\"color: #000000;\">En croisant et en segmentant toutes les donn\u00e9es \u00e9nerg\u00e9tiques, <a style=\"color: #000000;\" href=\"https:\/\/www.deepki.com\/fr\/blog\/clustering-donnees\/\">le clustering<\/a> appliqu\u00e9 au r\u00e9seau des boulangeries Paul a permis de discerner une typologie particuli\u00e8re de sites ayant une forte consommation en heures creuses. L\u2019analyse a mis en lumi\u00e8re un probl\u00e8me de r\u00e9gulation n\u00e9cessitant le r\u00e9glage de nouveaux fours tout juste install\u00e9s et mal programm\u00e9s.<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>Classification VS. R\u00e9gression<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><strong>Parmi les m\u00e9thodes d\u2019apprentissage supervis\u00e9, on distingue les cas de classification et de r\u00e9gression. Encore une fois, la diff\u00e9rence tient dans le type de donn\u00e9es analys\u00e9es.<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Dans le cadre de la classification<\/strong>, les \u00e9tiquettes sont des classes ou des cat\u00e9gories prenant un nombre de valeurs finies.<\/span><br \/><span style=\"color: #000000;\">Par exemple, dans un parc de b\u00e2timents, on peut imaginer qu\u2019il y ait trois types de sites\u00a0: banque, si\u00e8ge social et guichet automatique de billets. Un mod\u00e8le de classification cherchera \u00e0 pr\u00e9dire la typologie de chaque site \u00e0 partir de ses caract\u00e9ristiques (surface, nombre d\u2019employ\u00e9s, g\u00e9olocalisation, consommation \u00e9nerg\u00e9tique etc.).<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Dans le cadre de la r\u00e9gression,<\/strong> les \u00e9tiquettes sont de nature quantitative et prennent un nombre de valeurs potentiellement infinies.<\/span><br \/><span style=\"color: #000000;\">Par exemple, \u00e0 partir des caract\u00e9ristiques d\u2019un site, on peut pr\u00e9dire sa consommation. Cette valeur peut \u00eatre comprise entre 0 et l\u2019infini (et pas seulement 10 kWh, 1000 kWh ou 10 000 kWh par exemple).<\/span><\/p>\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/www.deepki.com\/web\/wp-content\/uploads\/2020\/05\/citation-machine-learning-1024x395.png\" alt=\"Le machine learning expliqu\u00e9 par Giulia Caputo\" class=\"wp-image-6259\"\/><\/figure>\n\n\n\n<p><strong>Avec les <a href=\"https:\/\/www.deepki.com\/fr\/glossaire\/algorithme\/\">algorithmes<\/a> de machine learning, il est d\u00e9sormais possible de d\u00e9tecter des pistes d\u2019am\u00e9lioration \u00e9nerg\u00e9tique simplement \u00e0 partir d\u2019une analyse de donn\u00e9es existantes. La m\u00e9thode employ\u00e9e d\u00e9pend surtout des donn\u00e9es dont on dispose et de leur structuration. Mais, quelle que soit la m\u00e9thode appliqu\u00e9e, le machine learning peut vous apporter beaucoup pour am\u00e9liorer la performance \u00e9nerg\u00e9tique des b\u00e2timents que vous g\u00e9rez.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tout d\u2019abord, qu\u2019est-ce que le machine learning ? Nous le d\u00e9finirons ici comme un ensemble de m\u00e9thodes qui, apr\u00e8s l\u2019analyse et l\u2019interpr\u00e9tation automatiques de donn\u00e9es, permet \u00e0 un syst\u00e8me de r\u00e9pondre \u00e0 un probl\u00e8me pr\u00e9cis face \u00e0 un nouveau jeu de donn\u00e9es. En machine learning, on distingue deux fa\u00e7ons de poser un probl\u00e8me : l\u2019apprentissage [&hellip;]<\/p>\n","protected":false},"author":44,"featured_media":593,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[118,120],"tags":[],"class_list":["post-592","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-fr","category-performance-environnementale"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Le machine learning, comment \u00e7a marche ? - Deepki<\/title>\n<meta name=\"description\" content=\"En machine learning, on distingue deux fa\u00e7ons de poser un probl\u00e8me : l\u2019apprentissage supervis\u00e9 VS non-supervis\u00e9 et les m\u00e9thodes de classification VS r\u00e9gression. Comment utilise-t-on ces m\u00e9thodes et quelles r\u00e9ponses peuvent-elles apporter en mati\u00e8re de performance \u00e9nerg\u00e9tique ? Pr\u00e9sentation de diff\u00e9rentes approches du machine learning illustr\u00e9es par des applications concr\u00e8tes.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.deepki.com\/fr\/blog\/machine-learning-comment-ca-marche\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Le machine learning, comment \u00e7a marche ? - Deepki\" \/>\n<meta property=\"og:description\" content=\"En machine learning, on distingue deux fa\u00e7ons de poser un probl\u00e8me : l\u2019apprentissage supervis\u00e9 VS non-supervis\u00e9 et les m\u00e9thodes de classification VS r\u00e9gression. 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