{"id":5885,"date":"2023-09-08T15:17:50","date_gmt":"2023-09-08T13:17:50","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5885"},"modified":"2024-12-02T17:56:55","modified_gmt":"2024-12-02T16:56:55","slug":"uniwin-adb","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-adb\/","title":{"rendered":"Uniwin – ADB"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Analyse Discriminante Bay\u00e9sienne<\/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\u2019Analyse Discriminante Bay\u00e9sienne (ADB) permet de construire \u00e0 partir d\u2019un ensemble de variables quantitatives et d\u2019une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus), des fonctions discriminantes qui d\u00e9finissent une r\u00e8gle de d\u00e9cision optimale \u00e0 partir de laquelle on peut affecter des individus de validation et de pr\u00e9vision aux diff\u00e9rents groupes.<\/p>\n

Cette technique suppose que l\u2019on connaisse a priori les probabilit\u00e9s d\u2019appartenance aux diff\u00e9rents groupes et que les donn\u00e9es suivent une loi multi-normale.<\/p>\n

La m\u00e9thode propos\u00e9e permet de traiter les cas lin\u00e9aire (\u00e9galit\u00e9 des matrices de variances) et quadratique (non-\u00e9galit\u00e9 des matrices de variances).<\/p>\n

L’entr\u00e9e des probabilit\u00e9s a priori est propos\u00e9e. Par d\u00e9faut, le syst\u00e8me utilise les probabilit\u00e9s issues des fr\u00e9quences des groupes dans les donn\u00e9es entr\u00e9es.<\/p>\n

En fonction des donn\u00e9es et des param\u00e8tres d\u00e9finis par l\u2019utilisateur, l\u2019analyse ADB r\u00e9alise automatiquement les \u00e9tudes de la population d\u2019apprentissage et des \u00e9ventuelles populations de validation et de pr\u00e9vision.<\/p>\n

De fa\u00e7on plus pr\u00e9cise, la m\u00e9thode peut se d\u00e9composer en trois \u00e9tapes. Supposons une population de n individus. D\u00e9coupons cette population en trois sous-populations de tailles n1<\/sub>, n2<\/sub>\u00a0et n3<\/sub>\u00a0avec n1<\/sub>\u00a0+ n2 +<\/sub>\u00a0n3<\/sub>\u00a0= n. Les trois \u00e9tapes sont \u00a0une \u00e9tude initiale sur la population d\u2019apprentissage de taille n1<\/sub>\u00a0puis\u00a0\u00a0une \u00e9tude de validation sur la population de validation de taille n2<\/sub>\u00a0et enfin\u00a0\u00a0une \u00e9tude prospective sur une population de pr\u00e9vision de taille n3<\/sub>.<\/p>\n

Des tableaux r\u00e9sum\u00e9s et d\u00e9taill\u00e9s des classements sont calcul\u00e9s.<\/p>\n

Le trac\u00e9 de plans factoriels et un rapport g\u00e9n\u00e9ral de synth\u00e8se sont propos\u00e9s.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6418″ 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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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Probabilit\u00e9s initiales<\/p>\n<\/td>\n<\/tr>\n

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Centro\u00efdes des groupes et global<\/p>\n<\/td>\n<\/tr>\n

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Matrice des covariances globales<\/p>\n<\/td>\n<\/tr>\n

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Matrice des corr\u00e9lations globales<\/p>\n<\/td>\n<\/tr>\n

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Covariances par groupe<\/p>\n<\/td>\n<\/tr>\n

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Corr\u00e9lations par groupe<\/p>\n<\/td>\n<\/tr>\n

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Covariances intra-groupes<\/p>\n<\/td>\n<\/tr>\n

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Corr\u00e9lations intra-groupes<\/p>\n<\/td>\n<\/tr>\n

\n

Distances de Mahalanobis, valeurs des Fishers et tests de signification<\/p>\n<\/td>\n<\/tr>\n

\n

Tableau des inerties<\/p>\n<\/td>\n<\/tr>\n

\n

Test de Pillai<\/p>\n<\/td>\n<\/tr>\n

\n

Test de Box<\/p>\n<\/td>\n<\/tr>\n

\n

Fonctions discriminantes standardis\u00e9es<\/p>\n<\/td>\n<\/tr>\n

\n

Fonctions discriminantes non standardis\u00e9es<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sultats pour les variables<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sultats pour les individus<\/p>\n<\/td>\n<\/tr>\n

\n

Coordonn\u00e9es des centres des groupes<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficients de la fonction de classement lin\u00e9aire (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficients de la fonction de classement quadratique (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

D\u00e9tails du classement (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

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

\n

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

\n

Statistiques des groupes observ\u00e9s (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

Statistiques des groupes pr\u00e9vus (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

\n

D\u00e9tails du 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

Diagramme des inerties<\/p>\n<\/td>\n<\/tr>\n

\n

Cercle factoriel des corr\u00e9lations des variables (points)<\/p>\n<\/td>\n<\/tr>\n

\n

Cercle factoriel des corr\u00e9lations des variables (points + lignes)<\/p>\n<\/td>\n<\/tr>\n

\n

Plan factoriel du jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel du jeu de validation<\/p>\n<\/td>\n<\/tr>\n

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Plan factoriel du jeu de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

\n

Plan factoriel des jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n

\n

Plan factoriel des jeux d’apprentissage et de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de la matrice de confusion pour le jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique de la matrice de confusion pour le jeu de validation<\/p>\n<\/td>\n<\/tr>\n

\n

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

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Courbe ROC pour le jeu 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%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FAnalyse%2520discriminante%2520bay%25e9sienne.pdf|title:UNIWIN%20-%20ADB\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\u2019Analyse Discriminante Bay\u00e9sienne (ADB) permet de construire \u00e0 partir d\u2019un ensemble de variables quantitatives et d\u2019une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus), des fonctions discriminantes qui d\u00e9finissent une r\u00e8gle de d\u00e9cision optimale \u00e0 partir de laquelle on peut affecter des individus de validation et de pr\u00e9vision aux diff\u00e9rents groupes. Cette…<\/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-5885","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - ADB - 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-adb\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - ADB - 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\u2019Analyse Discriminante Bay\u00e9sienne (ADB) permet de construire \u00e0 partir d\u2019un ensemble de variables quantitatives et d\u2019une variable qualitative d\u00e9coupant la population en plusieurs groupes (2 ou plus), des fonctions discriminantes qui d\u00e9finissent une r\u00e8gle de d\u00e9cision optimale \u00e0 partir de laquelle on peut affecter des individus de validation et de pr\u00e9vision aux diff\u00e9rents groupes. 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