{"id":7354,"date":"2026-07-09T10:10:34","date_gmt":"2026-07-09T08:10:34","guid":{"rendered":"https:\/\/francestat.com\/?page_id=7354"},"modified":"2026-07-09T11:30:25","modified_gmt":"2026-07-09T09:30:25","slug":"uniwin-gp","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-gp\/","title":{"rendered":"Uniwin – GP"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Processus gaussiens<\/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 css=\u00a0\u00bb\u00a0\u00bb]La proc\u00e9dure Processus gaussiens impl\u00e9mente une m\u00e9thode d’apprentissage machine pour pr\u00e9voir des observations \u00e0 partir de donn\u00e9es. Les processus gaussiens reposent sur l’hypoth\u00e8se que les observations adjacentes s’informent mutuellement. Plus pr\u00e9cis\u00e9ment, il est suppos\u00e9 que les variables observ\u00e9es suivent une loi normale et que leur couplage s’effectue par l’interm\u00e9diaire de la matrice de covariance d’une distribution normale.<\/p>\n

La proc\u00e9dure cr\u00e9e des mod\u00e8les de deux formes :<\/p>\n

    \n
  1. Mod\u00e8les de classement qui divisent des observations en classes en se basant sur les caract\u00e9ristiques observ\u00e9es.<\/li>\n
  2. Mod\u00e8les de r\u00e9gression qui pr\u00e9voient la valeur d’une variable cible.<\/li>\n<\/ol>\n

    Les observations sont classiquement divis\u00e9es en trois jeux : un jeu d’apprentissage utilis\u00e9 pour construire le mod\u00e8le, un jeu de validation, pour lequel la classe ou la valeur est connue, utilis\u00e9 pour valider le mod\u00e8le et un jeu de pr\u00e9vision, pour lequel la classe ou la valeur n’est pas connue, utilis\u00e9 pour faire les pr\u00e9visions d\u00e9sir\u00e9es.<\/p>\n

    La variable cible et les caract\u00e9ristiques pr\u00e9dictives peuvent \u00eatre qualitatives ou quantitatives.<\/p>\n

    Les variables quantitatives sont automatiquement centr\u00e9es et r\u00e9duites.<\/p>\n

    Cette proc\u00e9dure est bas\u00e9e sur le package R \u2018kernlab\u2019.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb7379″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb css=\u00a0\u00bb\u00a0\u00bb][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]Tableaux<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Param\u00e8tres et erreurs<\/span><\/td>\n<\/tr>\n
    Importance des variables explicatives<\/span><\/td>\n<\/tr>\n
    Valeurs observ\u00e9es, pr\u00e9vues et probabilit\u00e9s (classement, apprentissage)<\/span><\/td>\n<\/tr>\n
    Matrice de confusion (classement, apprentissage)<\/span><\/td>\n<\/tr>\n
    Sensibilit\u00e9, sp\u00e9cificit\u00e9 (classement, apprentissage)<\/span><\/td>\n<\/tr>\n
    Valeurs observ\u00e9es, pr\u00e9vues et probabilit\u00e9s (classement, validation)<\/span><\/td>\n<\/tr>\n
    Matrice de confusion (classement, validation)<\/td>\n<\/tr>\n
    Sensibilit\u00e9, sp\u00e9cificit\u00e9 (classement, validation)<\/td>\n<\/tr>\n
    Valeurs pr\u00e9vues et probabilit\u00e9s (classement, pr\u00e9vision)<\/span><\/td>\n<\/tr>\n
    Valeurs observ\u00e9es, pr\u00e9vues, r\u00e9sidus et intervalle de confiance (r\u00e9gression, apprentissage)<\/td>\n<\/tr>\n
    Valeurs observ\u00e9es, pr\u00e9vues, r\u00e9sidus et intervalle de confiance (r\u00e9gression, validation)<\/span><\/td>\n<\/tr>\n
    Valeurs pr\u00e9vues et intervalle de confiance (r\u00e9gression, pr\u00e9vision)<\/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\n\n\n\n\n
    Graphique des erreurs (apprentissage, validation crois\u00e9e)<\/span><\/td>\n<\/tr>\n
    Graphique de l’importance des variables explicatives<\/span><\/td>\n<\/tr>\n
    Nuage de points (apprentissage)<\/td>\n<\/tr>\n
    Nuage de points (validation)<\/td>\n<\/tr>\n
    Nuage de points (pr\u00e9vision)<\/td>\n<\/tr>\n
    Graphique des fronti\u00e8res (apprentissage)<\/td>\n<\/tr>\n
    Graphique des fronti\u00e8res (validation)<\/span><\/td>\n<\/tr>\n
    Diagramme de la matrice de confusion (classement, apprentissage)<\/span><\/td>\n<\/tr>\n
    Diagramme de la matrice de confusion (classement, validation)<\/span><\/td>\n<\/tr>\n
    Courbes ROC (classement, apprentissage)\u00a0<\/span><\/td>\n<\/tr>\n
    Courbes ROC (classement, validation)<\/span><\/td>\n<\/tr>\n
    Pr\u00e9vus versus observ\u00e9s (r\u00e9gression, apprentissage)<\/span><\/td>\n<\/tr>\n
    Pr\u00e9vus versus observ\u00e9s (r\u00e9gression, validation)<\/span><\/td>\n<\/tr>\n
    R\u00e9sidus versus pr\u00e9vus (r\u00e9gression, apprentissage)<\/span><\/td>\n<\/tr>\n
    R\u00e9sidus versus pr\u00e9vus (r\u00e9gression, validation)<\/span><\/td>\n<\/tr>\n
    Pr\u00e9vus versus X avec intervalle de confiance (r\u00e9gression, apprentissage)<\/span><\/td>\n<\/tr>\n
    Pr\u00e9vus versus X avec intervalle de confiance (r\u00e9gression, validation)<\/span><\/td>\n<\/tr>\n
    Pr\u00e9vus versus X avec intervalle de confiance (r\u00e9gression, pr\u00e9vision)<\/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 css=\u00a0\u00bb\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FProcessus%20gaussiens.pdf|title:UNIWIN%20-%20GP\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n<\/div>","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 css=\u00a0\u00bb\u00a0\u00bb]La proc\u00e9dure Processus gaussiens impl\u00e9mente une m\u00e9thode d’apprentissage machine pour pr\u00e9voir des observations \u00e0 partir de donn\u00e9es. Les processus gaussiens reposent sur l’hypoth\u00e8se que les observations adjacentes s’informent mutuellement. Plus pr\u00e9cis\u00e9ment, il est suppos\u00e9 que les variables observ\u00e9es suivent une loi normale et que leur couplage s’effectue par l’interm\u00e9diaire de la matrice de covariance…<\/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-7354","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - GP - 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-gp\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - GP - 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 css=\u00a0\u00bb\u00a0\u00bb]La proc\u00e9dure Processus gaussiens impl\u00e9mente une m\u00e9thode d’apprentissage machine pour pr\u00e9voir des observations \u00e0 partir de donn\u00e9es. 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