{"id":36391,"date":"2026-05-09T11:14:56","date_gmt":"2026-05-09T11:14:56","guid":{"rendered":"https:\/\/aisuperior.com\/?p=36391"},"modified":"2026-05-09T11:14:56","modified_gmt":"2026-05-09T11:14:56","slug":"predictive-analytics-in-tableau","status":"publish","type":"post","link":"https:\/\/aisuperior.com\/fr\/predictive-analytics-in-tableau\/","title":{"rendered":"Analyse pr\u00e9dictive dans Tableau\u00a0: Guide 2026"},"content":{"rendered":"<p><b>R\u00e9sum\u00e9 rapide\u00a0:<\/b><span style=\"font-weight: 400;\"> L&#039;analyse pr\u00e9dictive dans Tableau exploite des fonctions int\u00e9gr\u00e9es telles que MODEL_PERCENTILE et MODEL_QUANTILE pour pr\u00e9voir les r\u00e9sultats futurs \u00e0 l&#039;aide de mod\u00e8les de r\u00e9gression lin\u00e9aire. Tableau Cloud, Desktop, Public et Server prennent en charge la mod\u00e9lisation pr\u00e9dictive native sans n\u00e9cessiter d&#039;int\u00e9grations externes, ainsi que l&#039;int\u00e9gration d&#039;Einstein Discovery pour les sc\u00e9narios avanc\u00e9s. Les entreprises peuvent identifier les valeurs aberrantes, estimer les valeurs manquantes et pr\u00e9dire les p\u00e9riodes futures directement dans leurs visualisations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L&#039;analyse pr\u00e9dictive transforme les donn\u00e9es historiques en pr\u00e9visions exploitables. Tableau a \u00e9volu\u00e9 au-del\u00e0 de la simple visualisation\u00a0: c&#039;est d\u00e9sormais un outil pr\u00e9dictif puissant qui permet aux analystes de cr\u00e9er des mod\u00e8les statistiques directement depuis leurs tableaux de bord.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La plateforme utilise la r\u00e9gression lin\u00e9aire pour faire ressortir les tendances et les relations cach\u00e9es dans les donn\u00e9es. Deux calculs de table fondamentaux sous-tendent cette fonctionnalit\u00e9.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Comprendre les fonctions de mod\u00e9lisation pr\u00e9dictive de Tableau<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Tableau int\u00e8gre des fonctionnalit\u00e9s natives de mod\u00e9lisation pr\u00e9dictive dans Tableau Cloud, Tableau Desktop, Tableau Public et Tableau Server. Le syst\u00e8me repose sur trois fonctions principales qui g\u00e8rent des t\u00e2ches de pr\u00e9vision distinctes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La fonction MODEL_PERCENTILE renvoie la probabilit\u00e9 (entre 0 et 1) que la valeur attendue soit inf\u00e9rieure ou \u00e9gale \u00e0 la valeur observ\u00e9e. Elle calcule la fonction de distribution pr\u00e9dictive a posteriori, indiquant ainsi la position de votre point de donn\u00e9es dans l&#039;intervalle pr\u00e9dit.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La fonction MODEL_QUANTILE fonctionne \u00e0 l&#039;inverse. Elle renvoie la valeur num\u00e9rique cible \u00e0 un quantile sp\u00e9cifi\u00e9 de l&#039;intervalle probable. Cette fonction est id\u00e9ale lorsque vous avez besoin de valeurs num\u00e9riques plut\u00f4t que de probabilit\u00e9s.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">MODEL_EXPECTATION renvoie la valeur num\u00e9rique attendue (la moyenne de la distribution sous-jacente) pour l&#039;expression cible en fonction des pr\u00e9dicteurs.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Fonction<\/b><\/td>\n<td><b>Retours<\/b><\/td>\n<td><b>Id\u00e9al pour<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>POURCENTILE DU MOD\u00c8LE<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Probabilit\u00e9 (0-1)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Identification des valeurs aberrantes, d\u00e9tection des anomalies<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>MOD\u00c8LE_QUANTILE<\/b><\/td>\n<td><span style=\"font-weight: 400;\">valeur num\u00e9rique<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Estimation des fourchettes, pr\u00e9visions futures<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>MOD\u00c8LE_ATTENDANCE<\/b><\/td>\n<td><span style=\"font-weight: 400;\">valeur num\u00e9rique<\/span><\/td>\n<td><span style=\"font-weight: 400;\">R\u00e9sultat moyen, tendance g\u00e9n\u00e9rale de r\u00e9f\u00e9rence<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">La syntaxe suit un mod\u00e8le coh\u00e9rent. MODEL_PERCENTILE accepte les sp\u00e9cifications de mod\u00e8le, les expressions cibles et les expressions de pr\u00e9dicteurs. La sp\u00e9cification du mod\u00e8le est facultative\u00a0; par d\u00e9faut, Tableau utilise la r\u00e9gression lin\u00e9aire si elle est omise.<\/span><\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-35586\" src=\"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior.webp\" alt=\"\" width=\"434\" height=\"116\" srcset=\"https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior.webp 434w, https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior-300x80.webp 300w, https:\/\/aisuperior.com\/wp-content\/uploads\/2026\/04\/Superior-18x5.webp 18w\" sizes=\"(max-width: 434px) 100vw, 434px\" \/><\/p>\n<h2><span style=\"font-weight: 400;\">Utilisez l&#039;analyse pr\u00e9dictive avec l&#039;IA sup\u00e9rieure<\/span><\/h2>\n<p><a href=\"https:\/\/aisuperior.com\/fr\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">IA sup\u00e9rieure<\/span><\/a><span style=\"font-weight: 400;\"> Ce syst\u00e8me permet de connecter les mod\u00e8les pr\u00e9dictifs \u00e0 des outils de reporting comme Tableau afin d&#039;utiliser directement les r\u00e9sultats dans des tableaux de bord. L&#039;accent est mis sur la cr\u00e9ation de mod\u00e8les ind\u00e9pendants et l&#039;int\u00e9gration des r\u00e9sultats dans des outils de BI pour une utilisation concr\u00e8te.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Vous souhaitez ajouter des fonctionnalit\u00e9s d&#039;analyse pr\u00e9dictive \u00e0 Tableau\u00a0?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI Superior peut vous aider avec\u00a0:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">construction de mod\u00e8les pr\u00e9dictifs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">connecter les mod\u00e8les aux outils de BI<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">int\u00e9grer les r\u00e9sultats dans les tableaux de bord<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">perfectionnement des mod\u00e8les en fonction des retours d&#039;information<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">\ud83d\udc49 <\/span><a href=\"https:\/\/aisuperior.com\/fr\/contact\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Contactez l&#039;IA sup\u00e9rieure<\/span><\/a><span style=\"font-weight: 400;\"> pour discuter de votre projet, de vos donn\u00e9es et de votre approche de mise en \u0153uvre<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Applications pratiques<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Ces fonctions permettent de r\u00e9soudre des probl\u00e8mes concrets d&#039;entreprise. L&#039;identification des valeurs aberrantes devient simple\u00a0: MODEL_PERCENTILE signale les points de donn\u00e9es pr\u00e9sentant des scores de probabilit\u00e9 extr\u00eames. Les valeurs proches de 0 ou de 1 indiquent des observations tr\u00e8s \u00e9loign\u00e9es de la distribution attendue.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L&#039;estimation des donn\u00e9es \u00e9parses ou manquantes fonctionne diff\u00e9remment. Lorsque les ensembles de donn\u00e9es pr\u00e9sentent des lacunes, les fonctions pr\u00e9dictives les comblent en se basant sur les relations avec d&#039;autres variables. Cette m\u00e9thode est plus performante que les simples moyennes, car le mod\u00e8le tient compte des corr\u00e9lations entre plusieurs pr\u00e9dicteurs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les pr\u00e9visions de s\u00e9ries temporelles \u00e9tendent les axes temporels dans le futur. Cr\u00e9ez un calcul pour les mois \u00e0 venir, puis appliquez MODEL_QUANTILE pour projeter les ventes, le chiffre d&#039;affaires ou la demande. D&#039;apr\u00e8s les donn\u00e9es disponibles, l&#039;application syst\u00e9matique de l&#039;analyse de donn\u00e9es a permis d&#039;observer une augmentation de la valeur vie client, comme en t\u00e9moigne la hausse de 40 % enregistr\u00e9e par la plateforme logistique e-commerce Parcel Perform.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Types de mod\u00e8les et s\u00e9lection<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Tableau prend en charge la r\u00e9gression lin\u00e9aire, la r\u00e9gression lin\u00e9aire r\u00e9gularis\u00e9e et la r\u00e9gression par processus gaussien. Chaque mod\u00e8le g\u00e8re des sc\u00e9narios diff\u00e9rents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La r\u00e9gression lin\u00e9aire (m\u00e9thode par d\u00e9faut) convient lorsque les pr\u00e9dicteurs ont une relation lin\u00e9aire avec la variable cible et ne sont pas affect\u00e9s par les m\u00eames conditions sous-jacentes. Elle est rapide et interpr\u00e9table.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La r\u00e9gression lin\u00e9aire r\u00e9gularis\u00e9e pr\u00e9vient le surapprentissage en pr\u00e9sence d&#039;un grand nombre de pr\u00e9dicteurs. Le param\u00e8tre de r\u00e9gularisation limite la taille des coefficients, am\u00e9liorant ainsi la g\u00e9n\u00e9ralisation \u00e0 de nouvelles donn\u00e9es.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La r\u00e9gression par processus gaussien mod\u00e9lise les relations non lin\u00e9aires et fournit des estimations d&#039;incertitude. Plus gourmande en ressources de calcul, elle permet de d\u00e9tecter des sch\u00e9mas complexes que les mod\u00e8les lin\u00e9aires ne parviennent pas \u00e0 saisir.<\/span><\/p>\n<table>\n<thead>\n<tr>\n<th><b>Type de mod\u00e8le<\/b><\/th>\n<th><b>Cas d&#039;utilisation<\/b><\/th>\n<th><b>Co\u00fbt de calcul<\/b><b>\u00a0<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">R\u00e9gression lin\u00e9aire<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Relations lin\u00e9aires, peu de pr\u00e9dicteurs<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Faible<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Lin\u00e9aire r\u00e9gularis\u00e9<\/span><\/td>\n<td><span style=\"font-weight: 400;\">De nombreux pr\u00e9dicteurs, risque de surapprentissage<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Moyen<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Processus gaussien<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Mod\u00e8les non lin\u00e9aires, incertitude n\u00e9cessaire<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Haut<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span style=\"font-weight: 400;\">Int\u00e9gration de la d\u00e9couverte Einstein<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Pour les sc\u00e9narios avanc\u00e9s, Tableau s&#039;int\u00e8gre \u00e0 Einstein Discovery. Cela n\u00e9cessite une licence suppl\u00e9mentaire\u00a0: une licence Einstein Discovery dans Tableau, une licence CRM Analytics Plus ou une licence Einstein Predictions.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Einstein Discovery int\u00e8gre des mod\u00e8les pr\u00e9dictifs bas\u00e9s sur l&#039;IA aux tableaux de bord Tableau. Connectez-vous \u00e0 l&#039;extension analytique, interagissez avec les mod\u00e8les ou int\u00e9grez des pr\u00e9dictions via des scripts de calcul de table. La plateforme prend en charge des pr\u00e9dictions dynamiques \u00e0 la demande, qui s&#039;actualisent en fonction des filtrages et de l&#039;exploration des donn\u00e9es par l&#039;utilisateur.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Les organismes de sant\u00e9 ont constat\u00e9 des am\u00e9liorations significatives de leurs r\u00e9sultats gr\u00e2ce aux applications d&#039;analyse pr\u00e9dictive. Les entreprises de m\u00e9dias ont utilis\u00e9 l&#039;analyse pr\u00e9dictive pour optimiser leurs strat\u00e9gies d&#039;acquisition de clients. Ces r\u00e9sultats sont le fruit d&#039;un ciblage pr\u00e9cis rendu possible par les mod\u00e8les pr\u00e9dictifs.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Param\u00e8tres optionnels<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Einstein Discovery prend en charge des param\u00e8tres optionnels qui contr\u00f4lent les r\u00e9sultats. Le param\u00e8tre maxMiddleValues sp\u00e9cifie le nombre de pr\u00e9dicteurs les plus pertinents renvoy\u00e9s dans la r\u00e9ponse\u00a0; il est utile pour comprendre quels facteurs influencent les pr\u00e9dictions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Le param\u00e8tre maxPrescriptions d\u00e9finit le nombre maximal d&#039;am\u00e9liorations affich\u00e9es. Il fonctionne avec les mod\u00e8les de r\u00e9gression, de classification binaire et multiclasses.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Extensions analytiques<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">L&#039;API Analytics Extensions de Tableau permet aux \u00e9quipes d&#039;int\u00e9grer des mod\u00e8les d&#039;apprentissage automatique personnalis\u00e9s. Connectez-vous aux serveurs TabPy, RServe ou MATLAB pour ex\u00e9cuter des fonctions SCRIPT dans des champs calcul\u00e9s.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Cette approche convient aux organisations disposant de mod\u00e8les existants construits en Python ou en R. Les data scientists d\u00e9ploient les mod\u00e8les sur des serveurs d&#039;analyse, puis les analystes les appellent depuis Tableau \u00e0 l&#039;aide des fonctions SCRIPT_REAL, SCRIPT_INT, SCRIPT_STR ou SCRIPT_BOOL.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ce flux de travail dissocie le d\u00e9veloppement du mod\u00e8le de sa visualisation. Les data scientists travaillent dans leur environnement de pr\u00e9dilection tandis que les utilisateurs m\u00e9tiers interagissent via les tableaux de bord Tableau qu&#039;ils connaissent bien.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">FAQ<\/span><\/h2>\n<div class=\"schema-faq-code\">\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Quelle est la diff\u00e9rence entre la pr\u00e9vision et la mod\u00e9lisation pr\u00e9dictive dans Tableau\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">La pr\u00e9vision utilise le lissage exponentiel pour prolonger les s\u00e9ries temporelles. La mod\u00e9lisation pr\u00e9dictive utilise la r\u00e9gression pour \u00e9tablir des relations entre les variables et effectuer des pr\u00e9dictions. La pr\u00e9vision est automatique pour les donn\u00e9es temporelles\u00a0; la mod\u00e9lisation pr\u00e9dictive n\u00e9cessite la d\u00e9finition des variables cibles et des variables explicatives.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Puis-je utiliser l&#039;analyse pr\u00e9dictive dans Tableau Public\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Oui. Les fonctions MODEL_PERCENTILE et MODEL_QUANTILE fonctionnent dans Tableau Public, Desktop, Server et Cloud. Einstein Discovery n\u00e9cessite une licence payante et n&#039;est pas disponible dans l&#039;\u00e9dition publique.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Combien de pr\u00e9dicteurs puis-je inclure dans un mod\u00e8le\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">La r\u00e9gression lin\u00e9aire permet d&#039;utiliser plusieurs pr\u00e9dicteurs, mais ses limites pratiques d\u00e9pendent du volume de donn\u00e9es et des ressources de calcul. Commencez par les variables pr\u00e9sentant des relations claires avec la variable cible. Ajoutez d&#039;autres pr\u00e9dicteurs s&#039;ils am\u00e9liorent l&#039;ajustement du mod\u00e8le sans introduire de multicolin\u00e9arit\u00e9.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Les fonctions de mod\u00e9lisation pr\u00e9dictive n\u00e9cessitent-elles des int\u00e9grations externes\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Non. MODEL_PERCENTILE et MODEL_QUANTILE sont des calculs de table natifs qui fonctionnent sans connexion externe. Les extensions Analytics (Python, R, MATLAB) et Einstein Discovery sont optionnelles pour les sc\u00e9narios avanc\u00e9s.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Quels mod\u00e8les Tableau prend-il en charge pour l&#039;analyse pr\u00e9dictive\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Les fonctions natives prennent en charge la r\u00e9gression lin\u00e9aire, la r\u00e9gression lin\u00e9aire r\u00e9gularis\u00e9e et la r\u00e9gression par processus gaussien. Gr\u00e2ce aux extensions Analytics, les \u00e9quipes peuvent int\u00e9grer tout mod\u00e8le d\u00e9ployable sur les serveurs Python, R ou MATLAB.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Comment choisir entre MODEL_PERCENTILE et MODEL_QUANTILE\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Utilisez MODEL_PERCENTILE lorsque vous avez besoin de scores de probabilit\u00e9\u00a0; c\u2019est id\u00e9al pour la d\u00e9tection de valeurs aberrantes ou le signalement d\u2019anomalies. Utilisez MODEL_QUANTILE lorsque vous avez besoin de valeurs pr\u00e9dites exactes\u00a0; c\u2019est plus adapt\u00e9 pour compl\u00e9ter les donn\u00e9es manquantes ou pr\u00e9voir des valeurs sp\u00e9cifiques.<\/p>\n<\/div>\n<\/div>\n<div class=\"faq-question\">\n<h3 class=\"faq-q\">Les mod\u00e8les pr\u00e9dictifs peuvent-ils se mettre \u00e0 jour automatiquement lors de l&#039;actualisation des donn\u00e9es\u00a0?<\/h3>\n<div>\n<p class=\"faq-a\">Oui. Les calculs pr\u00e9dictifs sont recalcul\u00e9s lorsque les donn\u00e9es sous-jacentes sont actualis\u00e9es. Le mod\u00e8le se reconstruit \u00e0 partir des donn\u00e9es actuelles, garantissant ainsi que les pr\u00e9dictions refl\u00e8tent les tendances les plus r\u00e9centes. Ceci est valable pour les fonctions natives et les extensions Analytics.<\/p>\n<h2><span style=\"font-weight: 400;\">Aller de l&#039;avant<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">L&#039;analyse pr\u00e9dictive dans Tableau comble le foss\u00e9 entre l&#039;analyse et la pr\u00e9vision. Les fonctions natives couvrent la plupart des cas d&#039;utilisation sans outils suppl\u00e9mentaires. Les extensions Einstein Discovery et Analytics \u00e9tendent les fonctionnalit\u00e9s pour r\u00e9pondre \u00e0 des besoins sp\u00e9cifiques.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Commencez par param\u00e9trer MODEL_PERCENTILE et MODEL_QUANTILE sur les tableaux de bord existants. Testez les pr\u00e9dictions par rapport aux r\u00e9sultats connus afin de valider la pr\u00e9cision du mod\u00e8le. Affinez la s\u00e9lection des pr\u00e9dicteurs en fonction de votre connaissance m\u00e9tier et des relations statistiques.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La force de la plateforme r\u00e9side dans son accessibilit\u00e9\u00a0: les analystes cr\u00e9ent des mod\u00e8les pr\u00e9dictifs via la m\u00eame interface que celle utilis\u00e9e pour les visualisations. Consultez la documentation officielle de Tableau pour conna\u00eetre les fonctionnalit\u00e9s disponibles et commencez d\u00e8s aujourd\u2019hui \u00e0 r\u00e9aliser des pr\u00e9visions.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Quick Summary: Predictive analytics in Tableau leverages built-in functions like MODEL_PERCENTILE and MODEL_QUANTILE to forecast future outcomes using linear regression models. Tableau Cloud, Desktop, Public, and Server support native predictive modeling without requiring external integrations, plus Einstein Discovery integration for advanced scenarios. Organizations can identify outliers, estimate missing values, and predict future time periods directly [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":36392,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-36391","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predictive Analytics in Tableau: 2026 Guide<\/title>\n<meta name=\"description\" content=\"Master predictive analytics in Tableau with MODEL_PERCENTILE and MODEL_QUANTILE functions. 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