{"id":5916,"date":"2023-09-08T15:54:03","date_gmt":"2023-09-08T13:54:03","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5916"},"modified":"2026-03-29T15:57:39","modified_gmt":"2026-03-29T13:57:39","slug":"uniwin-bayes","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/","title":{"rendered":"Uniwin – BAYES"},"content":{"rendered":"

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UNIWIN - Classement par la m\u00e9thode bay\u00e9sienne na\u00efve<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

La m\u00e9thode de classement bay\u00e9sienne na\u00efve est bas\u00e9e sur le th\u00e9or\u00e8me de Bayes avec l\u2019hypoth\u00e8se, dite na\u00efve, d\u2019ind\u00e9pendance conditionnelle entre toutes les paires de descripteurs par rapport aux valeurs de la variable \u00e0 pr\u00e9dire.<\/p>\n

Elle met en \u0153uvre un classifieur bay\u00e9sien na\u00eff appartenant \u00e0 la famille des classifieurs lin\u00e9aires. Un terme plus appropri\u00e9 pour le mod\u00e8le probabiliste sous-jacent pourrait \u00eatre mod\u00e8le \u00e0 caract\u00e9ristiques statistiquement ind\u00e9pendantes.<\/p>\n

En termes simples, un classifieur bay\u00e9sien na\u00eff suppose que l’existence d’une caract\u00e9ristique pour une classe est ind\u00e9pendante de l’existence d’autres caract\u00e9ristiques. Un fruit peut \u00eatre consid\u00e9r\u00e9 comme une pomme s’il est rouge, arrondi, et fait une dizaine de centim\u00e8tres. M\u00eame si ces caract\u00e9ristiques sont li\u00e9es dans la r\u00e9alit\u00e9, un classifieur bay\u00e9sien na\u00eff d\u00e9terminera que le fruit est une pomme en consid\u00e9rant ind\u00e9pendamment ses caract\u00e9ristiques de couleur, de forme et de taille.<\/p>\n

Malgr\u00e9 le mod\u00e8le de conception na\u00eff et les hypoth\u00e8ses de base extr\u00eamement simplistes, les classifieurs bay\u00e9siens na\u00effs ont fait preuve d’une efficacit\u00e9 plus que suffisante dans beaucoup de situations r\u00e9elles complexes. L’avantage du classifieur bay\u00e9sien na\u00eff est qu’il requiert relativement peu de donn\u00e9es d’entra\u00eenement pour estimer les param\u00e8tres n\u00e9cessaires au classement.<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R ‘naivebayes’.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb4010″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

Tableaux<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Probabilit\u00e9s a priori des classes<\/p>\n<\/td>\n<\/tr>\n

\n

Facteur de classement<\/p>\n<\/td>\n<\/tr>\n

\n

Informations sur les variables<\/p>\n<\/td>\n<\/tr>\n

\n

Exactitude (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

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Importance des variables<\/p>\n<\/td>\n<\/tr>\n

\n

Classement apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

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Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Classement validation<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Classement pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Graphiques des lois marginales<\/p>\n<\/td>\n<\/tr>\n

\n

Graphiques des lois conditionnelles<\/p>\n<\/td>\n<\/tr>\n

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Graphiques de l’importance des variables<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points donn\u00e9es d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points des donn\u00e9es de validation<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points des donn\u00e9es de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des fronti\u00e8res (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des fronti\u00e8res (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme de la matrice de confusion (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Diagramme de la matrice de confusion (validation)<\/p>\n<\/td>\n<\/tr>\n

\n

Courbe ROC pour les donn\u00e9es d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Courbe ROC pour les donn\u00e9es de validation<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:https%3A%2F%2Ffrancestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FM%25e9thode%2520bay%25e9sienne%2520na%25efve.pdf|title:UNIWIN%20-%20BAYES\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] La m\u00e9thode de classement bay\u00e9sienne na\u00efve est bas\u00e9e sur le th\u00e9or\u00e8me de Bayes avec l\u2019hypoth\u00e8se, dite na\u00efve, d\u2019ind\u00e9pendance conditionnelle entre toutes les paires de descripteurs par rapport aux valeurs de la variable \u00e0 pr\u00e9dire. Elle met en \u0153uvre un classifieur bay\u00e9sien na\u00eff appartenant \u00e0 la famille des classifieurs lin\u00e9aires. Un terme plus appropri\u00e9 pour…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5916","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - BAYES - FRANCESTAT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/francestat.com\/index.php\/uniwin-bayes\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - BAYES - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] La m\u00e9thode de classement bay\u00e9sienne na\u00efve est bas\u00e9e sur le th\u00e9or\u00e8me de Bayes avec l\u2019hypoth\u00e8se, dite na\u00efve, d\u2019ind\u00e9pendance conditionnelle entre toutes les paires de descripteurs par rapport aux valeurs de la variable \u00e0 pr\u00e9dire. Elle met en \u0153uvre un classifieur bay\u00e9sien na\u00eff appartenant \u00e0 la famille des classifieurs lin\u00e9aires. Un terme plus appropri\u00e9 pour…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-bayes\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-29T13:57:39+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-bayes\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-bayes\\\/\",\"name\":\"Uniwin - BAYES - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-08T13:54:03+00:00\",\"dateModified\":\"2026-03-29T13:57:39+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-bayes\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-bayes\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-bayes\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – BAYES\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"name\":\"FRANCESTAT\",\"description\":\"Logiciels, formations et services statistiques\",\"publisher\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/francestat.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\",\"name\":\"FRANCESTAT\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"contentUrl\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"width\":155,\"height\":51,\"caption\":\"FRANCESTAT\"},\"image\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Uniwin - BAYES - FRANCESTAT","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - BAYES - FRANCESTAT","og_description":"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text] La m\u00e9thode de classement bay\u00e9sienne na\u00efve est bas\u00e9e sur le th\u00e9or\u00e8me de Bayes avec l\u2019hypoth\u00e8se, dite na\u00efve, d\u2019ind\u00e9pendance conditionnelle entre toutes les paires de descripteurs par rapport aux valeurs de la variable \u00e0 pr\u00e9dire. Elle met en \u0153uvre un classifieur bay\u00e9sien na\u00eff appartenant \u00e0 la famille des classifieurs lin\u00e9aires. Un terme plus appropri\u00e9 pour…","og_url":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/","og_site_name":"FRANCESTAT","article_modified_time":"2026-03-29T13:57:39+00:00","twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/","url":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/","name":"Uniwin - BAYES - FRANCESTAT","isPartOf":{"@id":"https:\/\/francestat.com\/#website"},"datePublished":"2023-09-08T13:54:03+00:00","dateModified":"2026-03-29T13:57:39+00:00","breadcrumb":{"@id":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/francestat.com\/index.php\/uniwin-bayes\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/francestat.com\/index.php\/uniwin-bayes\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/francestat.com\/"},{"@type":"ListItem","position":2,"name":"Uniwin – BAYES"}]},{"@type":"WebSite","@id":"https:\/\/francestat.com\/#website","url":"https:\/\/francestat.com\/","name":"FRANCESTAT","description":"Logiciels, formations et services statistiques","publisher":{"@id":"https:\/\/francestat.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/francestat.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/francestat.com\/#organization","name":"FRANCESTAT","url":"https:\/\/francestat.com\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/francestat.com\/#\/schema\/logo\/image\/","url":"https:\/\/francestat.com\/wp-content\/uploads\/2018\/05\/logo_horizontal_small_francestat.jpg","contentUrl":"https:\/\/francestat.com\/wp-content\/uploads\/2018\/05\/logo_horizontal_small_francestat.jpg","width":155,"height":51,"caption":"FRANCESTAT"},"image":{"@id":"https:\/\/francestat.com\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5916","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/comments?post=5916"}],"version-history":[{"count":14,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5916\/revisions"}],"predecessor-version":[{"id":7165,"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/pages\/5916\/revisions\/7165"}],"wp:attachment":[{"href":"https:\/\/francestat.com\/index.php\/wp-json\/wp\/v2\/media?parent=5916"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}