{"id":5908,"date":"2023-09-08T15:45:12","date_gmt":"2023-09-08T13:45:12","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5908"},"modified":"2026-03-29T16:16:41","modified_gmt":"2026-03-29T14:16:41","slug":"uniwin-knn","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-knn\/","title":{"rendered":"Uniwin – KNN"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - M\u00e9thode des K plus proches voisins (classement, 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]L\u2019algorithme des K plus proches voisins (KNN) repose sur une id\u00e9e simple : on suppose que la valeur d\u2019une variable \u00e0 expliquer pour une observation doit \u00eatre proche des valeurs qu\u2019elle prend pour ses observations les plus voisines.<\/p>\n

Si la variable \u00e0 expliquer est qualitative, on affecte l\u2019observation \u00e0 la classe majoritaire parmi ses donn\u00e9es voisines.<\/p>\n

Si la variable \u00e0 expliquer est quantitative, on donne \u00e0 l\u2019observation la valeur moyenne observ\u00e9e de la variable \u00e0 expliquer pour ses donn\u00e9es voisines.<\/p>\n

Une plage de valeurs de K est pr\u00e9cis\u00e9e et la valeur optimale de K est d\u00e9termin\u00e9e par validation crois\u00e9e.<\/p>\n

Les r\u00e9sultats sont fournis pour les jeux d\u2019apprentissage, de validation et de pr\u00e9vision.<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R ‘caret’.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6360″ 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

D\u00e9termination du K optimal<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

Classement pour le jeu d’apprentissage (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion pour le jeu d’apprentissage (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Sensibilit\u00e9s, sp\u00e9cificit\u00e9s pour le jeu d’apprentissage (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Classement pour le jeu de validation (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de confusion pour le jeu de validation (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Sensibilit\u00e9s, sp\u00e9cificit\u00e9s pour le jeu de validation (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Classement pour le jeu de pr\u00e9vision (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Valeurs observ\u00e9es, pr\u00e9vues, r\u00e9sidus pour le jeu d’apprentissage (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

Valeurs observ\u00e9es, pr\u00e9vues, r\u00e9sidus pour le jeu de validation (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

Valeurs pr\u00e9vues pour le jeu de pr\u00e9vision (r\u00e9gression)<\/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\n\n\n
\n

Graphique de l’\u00e9volution de l’exactitude (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de l’\u00e9volution de l’erreur quadratique moyenne (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

Nuage de points du jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points du jeu de validation<\/p>\n<\/td>\n<\/tr>\n

\n

Nuage de points du jeu 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 pour le jeu d’apprentissage (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

Courbe ROC pour le jeu d’apprentissage (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Courbe ROC pour le jeu de validation (classement)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des valeurs pr\u00e9vues vs observ\u00e9es pour le jeu d’apprentissage (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des valeurs pr\u00e9vues vs observ\u00e9es pour le jeu de validation (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des r\u00e9sidus vs valeurs pr\u00e9vues pour le jeu d’apprentissage (r\u00e9gression)<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des r\u00e9sidus vs valeurs pr\u00e9vues pour le jeu de validation (r\u00e9gression)<\/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%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FM%25e9thode%2520KNN.pdf|title:UNIWIN%20-%20KNN\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]L\u2019algorithme des K plus proches voisins (KNN) repose sur une id\u00e9e simple : on suppose que la valeur d\u2019une variable \u00e0 expliquer pour une observation doit \u00eatre proche des valeurs qu\u2019elle prend pour ses observations les plus voisines. Si la variable \u00e0 expliquer est qualitative, on affecte l\u2019observation \u00e0 la classe majoritaire parmi ses donn\u00e9es…<\/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-5908","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - KNN - 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-knn\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - KNN - 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]L\u2019algorithme des K plus proches voisins (KNN) repose sur une id\u00e9e simple : on suppose que la valeur d\u2019une variable \u00e0 expliquer pour une observation doit \u00eatre proche des valeurs qu\u2019elle prend pour ses observations les plus voisines. Si la variable \u00e0 expliquer est qualitative, on affecte l\u2019observation \u00e0 la classe majoritaire parmi ses donn\u00e9es…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-knn\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-29T14:16:41+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-knn\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-knn\\\/\",\"name\":\"Uniwin - KNN - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-08T13:45:12+00:00\",\"dateModified\":\"2026-03-29T14:16:41+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-knn\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-knn\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-knn\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – KNN\"}]},{\"@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 - KNN - 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-knn\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - KNN - 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]L\u2019algorithme des K plus proches voisins (KNN) repose sur une id\u00e9e simple : on suppose que la valeur d\u2019une variable \u00e0 expliquer pour une observation doit \u00eatre proche des valeurs qu\u2019elle prend pour ses observations les plus voisines. 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