{"id":6500,"date":"2024-12-01T10:57:45","date_gmt":"2024-12-01T09:57:45","guid":{"rendered":"https:\/\/francestat.com\/?page_id=6500"},"modified":"2026-03-30T12:06:52","modified_gmt":"2026-03-30T10:06:52","slug":"uniwin-foret","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-foret\/","title":{"rendered":"Uniwin – FORET"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - For\u00eats al\u00e9atoires de d\u00e9cision et de r\u00e9gression<\/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]La proc\u00e9dure For\u00eats al\u00e9atoires cr\u00e9e des mod\u00e8les de deux formes : mod\u00e8les d\u00e9cisionnels qui d\u00e9coupent les observations en groupes bas\u00e9s sur les caract\u00e9ristiques observ\u00e9es et mod\u00e8les de r\u00e9gression qui pr\u00e9voient la valeur d\u2019une variable \u00e0 expliquer. Les mod\u00e8les sont \u00e9labor\u00e9s en construisant un grand nombre d\u2019arbres et en faisant la moyenne des pr\u00e9visions obtenues \u00e0 partir de ces arbres. Les arbres sont construits en utilisant une proc\u00e9dure similaire \u00e0 celle des arbres de d\u00e9cision et de r\u00e9gression, avec optimisation al\u00e9atoire des n\u0153uds et agr\u00e9gation de bootstrap (bagging). Les donn\u00e9es brutes sont utilis\u00e9es pour les calculs car la structure d\u2019un arbre n\u2019est pas impact\u00e9e par les habituelles
\ntransformations monotones des donn\u00e9es. Les observations sont d\u00e9coup\u00e9es en trois jeux : un jeu d\u2019apprentissage utilis\u00e9 pour construire les arbres, un jeu de validation et un jeu de pr\u00e9vision pour lequel les classes ou valeurs de la variable \u00e0 expliquer ne sont pas connues et doivent \u00eatre pr\u00e9vues. La variable \u00e0 expliquer est soit qualitative, soit quantitative, comme c\u2019est \u00e9galement le cas pour les variables explicatives.<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R \u2018randomForest\u2019.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6513″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb style=\u00a0\u00bbvc_box_border\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][vc_column_text]Tableaux<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Erreur de pr\u00e9vision (OOB) – classement<\/span><\/td>\n<\/tr>\n
Erreurs quadratiques moyennes et R-carr\u00e9s (OOB) – r\u00e9gression<\/span><\/td>\n<\/tr>\n
Importances des variables explicatives<\/span><\/td>\n<\/tr>\n
Utilisation des variables explicatives<\/span><\/td>\n<\/tr>\n
Nombres de noeuds terminaux dans les arbres<\/span><\/td>\n<\/tr>\n
Nombres de fois o\u00f9 chaque observation est OOB dans les arbres<\/span><\/td>\n<\/tr>\n
D\u00e9tail du classement pour les jeux d’apprentissage et de validation (OOB) – classement<\/span><\/td>\n<\/tr>\n
Matrices de confusion pour les jeux d’apprentissage et de validation (OOB) – classement<\/span><\/td>\n<\/tr>\n
Sensibilit\u00e9s, sp\u00e9cificit\u00e9s pour les jeux d’apprentissage et de validation (OOB) – classement<\/span><\/td>\n<\/tr>\n
D\u00e9tail du classement pour le jeu de pr\u00e9vision – classement<\/span><\/td>\n<\/tr>\n
Valeurs observ\u00e9es, estim\u00e9es et r\u00e9sidus pour les jeux d’apprentissage et de validaiton – r\u00e9gression<\/span><\/td>\n<\/tr>\n
Valeurs estim\u00e9es pour le jeu de pr\u00e9vision – r\u00e9gression<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Utilisation des variables explicatives<\/span><\/td>\n<\/tr>\n
Nombres de noeuds terminaux dans les arbres<\/span><\/td>\n<\/tr>\n
Importance des variables (erreur de classement et impuret\u00e9 des noeuds (indice de Gini)) – classement<\/span><\/td>\n<\/tr>\n
Importance des variables (erreur quadratique moyenne et impuret\u00e9 des noeuds (r\u00e9sidus)) – r\u00e9gression<\/span><\/td>\n<\/tr>\n
Graphique de l’erreur de pr\u00e9vision OOB – classement<\/span><\/td>\n<\/tr>\n
Nuages de points pour les jeux d’apprentissage, de validation et de pr\u00e9vision<\/span><\/td>\n<\/tr>\n
Graphiques des fronti\u00e8res pour les jeux d’apprentissage et de validation<\/span><\/td>\n<\/tr>\n
Graphiques des matrices de confusion pour les jeux d’apprentissage et de validation – classement<\/span><\/td>\n<\/tr>\n
Courbes ROC pour les jeux d’apprentissage et de validation – classement<\/span><\/td>\n<\/tr>\n
Graphique des erreurs quadratiques moyennes – r\u00e9gression\u00a0<\/span><\/td>\n<\/tr>\n
Graphique des R-carr\u00e9s – r\u00e9gression\u00a0<\/span><\/td>\n<\/tr>\n
Graphiques des valeurs estim\u00e9es vs observ\u00e9es pour les jeux d’apprentissage et de validation – r\u00e9gression<\/span><\/td>\n<\/tr>\n
Graphiques des r\u00e9sidus vs valeurs estim\u00e9es pour les jeux d’apprentissage et de validation – r\u00e9gression<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][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%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FFor%25eats%2520al%25e9atoires.pdf|title:UNIWIN%20-%20FORET\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 proc\u00e9dure For\u00eats al\u00e9atoires cr\u00e9e des mod\u00e8les de deux formes : mod\u00e8les d\u00e9cisionnels qui d\u00e9coupent les observations en groupes bas\u00e9s sur les caract\u00e9ristiques observ\u00e9es et mod\u00e8les de r\u00e9gression qui pr\u00e9voient la valeur d\u2019une variable \u00e0 expliquer. Les mod\u00e8les sont \u00e9labor\u00e9s en construisant un grand nombre d\u2019arbres et en faisant la moyenne des pr\u00e9visions obtenues \u00e0…<\/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-6500","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - FORET - 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-foret\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - FORET - 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 proc\u00e9dure For\u00eats al\u00e9atoires cr\u00e9e des mod\u00e8les de deux formes : mod\u00e8les d\u00e9cisionnels qui d\u00e9coupent les observations en groupes bas\u00e9s sur les caract\u00e9ristiques observ\u00e9es et mod\u00e8les de r\u00e9gression qui pr\u00e9voient la valeur d\u2019une variable \u00e0 expliquer. Les mod\u00e8les sont \u00e9labor\u00e9s en construisant un grand nombre d\u2019arbres et en faisant la moyenne des pr\u00e9visions obtenues \u00e0…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-foret\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-30T10:06:52+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=\"3 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-foret\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-foret\\\/\",\"name\":\"Uniwin - FORET - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2024-12-01T09:57:45+00:00\",\"dateModified\":\"2026-03-30T10:06:52+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-foret\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-foret\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-foret\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – FORET\"}]},{\"@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 - FORET - 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-foret\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - FORET - 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 proc\u00e9dure For\u00eats al\u00e9atoires cr\u00e9e des mod\u00e8les de deux formes : mod\u00e8les d\u00e9cisionnels qui d\u00e9coupent les observations en groupes bas\u00e9s sur les caract\u00e9ristiques observ\u00e9es et mod\u00e8les de r\u00e9gression qui pr\u00e9voient la valeur d\u2019une variable \u00e0 expliquer. 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