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6.47672L10.8327 12.7503L5.6084 17.4217C5.2699 17.7151 5.24733 18.2228 5.5407 18.5613C5.83407 18.8998 6.35311 18.9224 6.68033 18.629L11.9159 13.9464L12.9201 15.0409C13.8454 16.0564 15.0865 16.6093 16.418 16.6093C17.7495 16.6093 18.9906 16.0564 19.9159 15.0409L20.8637 14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-86fce3976b","additionalClasses":"headline-decoration","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cce0a3354d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa forêt aléatoire est l’un des algorithmes les plus performants et les plus populaires utilisés dans la création de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodèles de machine learning\u003C/a\u003E. Ce modèle d’apprentissage supervisé crée plusieurs arbres décisionnels, puis combine les prédictions à partir de ces derniers pour produire des résultats plus précis et plus fiables. La capacité de l’algorithme à contourner les problèmes liés aux données manquantes ou bruitées est l’une des principales raisons pour lesquelles il est couramment déployé dans des applications telles que la notation de crédit, la prévision de la demande et la classification d’images.\u003C/p\u003E\n\u003Cp\u003EDans ce guide, nous allons aborder le fonctionnement de la forêt aléatoire et pourquoi elle est un outil important pour la conception de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-models/\"\u003Emodèles d’IA\u003C/a\u003E et de machine learning fiables.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"container_115652453":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_1858830768":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a19c2e7683","additionalClasses":"headline-decoration","type":"heading2","lines":["Qu’est-ce qu’une forêt aléatoire ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0b14f81479","text":"\u003Cp\u003ELa forêt aléatoire est un algorithme de machine learning ensembliste qui crée de nombreux arbres décisionnels pendant sa période d’entraînement. Chaque arbre est entraîné sur un sous-jeu aléatoire du jeu de données d’entraînement, sélectionne un nombre déterminé d’attributs de données de manière aléatoire à partir de chaque point de décision de l’arbre, puis génère ses propres prédictions. \u003C/p\u003E\n\u003Cp\u003ELes modèles créés à l’aide de forêts aléatoires peuvent être utilisés à la fois pour la classification (détermination de la prédiction choisie par le plus grand nombre d’arbres) ou pour l’\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/regression-models/\"\u003Eanalyse de régression\u003C/a\u003E (moyenne des prédictions de tous les arbres). \u003C/p\u003E\n\u003Cp\u003EPar exemple, un modèle conçu pour classer les e-mails comme spams ou non analyserait les résultats de toutes les arborescences et choisirait la classification retenue par la majorité d’entre elles. En revanche, un modèle conçu pour prédire le prix des maisons calculerait la moyenne des résultats de tous les arbres.\u003C/p\u003E\n\u003Cp\u003ECette méthode réduit le risque que les prévisions extrêmes faussent les résultats finaux et offre des moyens simples de mesurer la confiance et la variabilité de chaque prédiction. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a54ea7d7e0","additionalClasses":"headline-decoration","type":"heading2","lines":["Comment la forêt aléatoire se compare aux arbres décisionnels"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-bceeeb30fa","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EÀ son niveau le plus élémentaire, une forêt aléatoire est un ensemble d’arbres de décision. Mais il existe de nombreuses différences pratiques entre le fonctionnement de ces deux approches.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Jeux de données\u003C/h3\u003E\n\u003Cp\u003EUn arbre décisionnel utilise le jeu de données d’entraînement et prend en compte toutes les fonctionnalités disponibles (attributs de données, tels que l’emplacement, la taille et l’âge d’une maison) dans ses prédictions. Une forêt aléatoire crée plusieurs arbres à partir de ce jeu de données et sélectionne des fonctionnalités de manière aléatoire pour générer des résultats.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Méthodologie de prédiction\u003C/h3\u003E\n\u003Cp\u003ELes arbres décisionnels suivent un chemin direct et génèrent une seule prédiction. Une forêt aléatoire obtient des prédictions de chaque arbre et génère une prédiction globale au moyen d’un comptage ou d’une moyenne des résultats.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Interprétabilité\u003C/h3\u003E\n\u003Cp\u003ELes arbres décisionnels utilisent une méthode simple à expliquer pour arriver à leurs prédictions. Une forêt aléatoire est beaucoup plus complexe, ce qui rend plus difficile l’explication des prédictions individuelles.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Ressources de calcul\u003C/h3\u003E\n\u003Cp\u003EUn arbre décisionnel est beaucoup plus simple, plus rapide à entraîner et consomme beaucoup moins de ressources de calcul et de mémoire. L’entraînement de plusieurs arbres dans une forêt aléatoire peut s’avérer coûteux en calcul et nécessiter un délai plus long.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Performances\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent être très précis, mais sont également sujets à des surapprentissages, ce qui amène un modèle à faire des prédictions moins précises lorsqu’il reçoit des données en dehors de son ensemble d’entraînement. Les arbres décisionnels sont également plus sensibles aux données manquantes ou bruitées. Les prédictions générées par les algorithmes de forêt aléatoire sont généralement considérées comme plus précises, stables et fiables.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-806b994933","additionalClasses":"headline-decoration","type":"heading2","lines":["Étapes de l’algorithme de forêt aléatoire"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f08d78821f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa forêt aléatoire crée des centaines d’arbres décisionnels, qui apprennent chacun à partir d’échantillons aléatoires différents de données d’entraînement et prennent en compte différentes combinaisons de caractéristiques des données. Ils combinent ensuite toutes leurs prédictions par vote ou par moyenne afin de produire un résultat plus précis et plus fiable que celui qu’un seul arbre pourrait obtenir.\u003C/p\u003E\n\u003Cp\u003EVoici les principales étapes que suit la forêt aléatoire, des données brutes à la prédiction finale :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Préparation des données\u003C/h3\u003E\n\u003Cp\u003EL’algorithme prend le jeu de données d’entraînement d’origine et le prépare pour le traitement. Tout nettoyage, formatage ou prétraitement nécessaire est effectué à ce stade.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Échantillonnage des données \u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire utilise une technique d’échantillonnage statistique appelée « bagging » (ou « bootstrap aggregating ») pour sélectionner des points de données au hasard pour chaque arbre, dont beaucoup sont répétés dans plusieurs arbres. Cela permet de s’assurer que chaque arbre voit une version légèrement différente des données d’entraînement.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Création de chaque arbre \u003C/h3\u003E\n\u003Cp\u003EChaque arbre est créé par division répétée du jeu de données afin de créer de nouvelles branches. Par exemple, si vous créez un arbre pour prédire si une personne est susceptible d’acheter une nouvelle voiture, il peut être divisé en fonction du fait que son revenu annuel est supérieur ou inférieur à 100 000 USD, puis à nouveau en fonction du fait qu’elle a plus de 30 ans. À chaque point de décision, l’algorithme sélectionne aléatoirement un sous-ensemble de caractéristiques disponibles et choisit celle qui établit la séparation la plus claire entre les différents résultats.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Croissance de la forêt \u003C/h3\u003E\n\u003Cp\u003EL’algorithme répète les étapes 2 et 3 entre 100 et 1000 fois pour créer une collection d’arbres décisionnels diversifiés. Chaque arbre apprend des schémas différents, car il examine des données distinctes et prend en compte des caractéristiques différentes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Réalisation de prédictions individuelles\u003C/h3\u003E\n\u003Cp\u003ELorsque de nouvelles données arrivent, chaque arbre de la forêt effectue sa propre prédiction de manière indépendante selon les règles décisionnelles qu’il a apprises. Il en résulte plusieurs prédictions distinctes pour la même entrée.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E6. Comptage ou calcul de la moyenne \u003C/h3\u003E\n\u003Cp\u003EPour les problèmes de classification, l’algorithme compte les votes de tous les arbres et sélectionne la classe qui a obtenu le plus de votes. Pour les problèmes de régression, il calcule la moyenne de toutes les prédictions d’arbres pour produire le résultat final.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E7. Obtention du résultat final\u003C/h3\u003E\n\u003Cp\u003EL’algorithme fournit la prédiction consolidée ainsi que des mesures de confiance optionnelles basées sur le degré de concordance entre les arbres individuels.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-08b2dfbd72","additionalClasses":"headline-decoration","type":"heading2","lines":["Principaux avantages du modèle de forêt aléatoire"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0277a48b39","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EQu’il soit utilisé pour la classification ou la régression aléatoire, le modèle de forêt aléatoire excelle dans la production de résultats précis à partir de jeux de données complexes avec un réglage minimal. Voici quelques-uns des principaux avantages qui font de la forêt aléatoire un algorithme incontournable pour les data scientists :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EOffre un niveau élevé de précision\u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire offre des performances prédictives constantes et fiables pour divers jeux de données et types de problèmes. La décision collective de centaines d’arbres produit généralement des résultats plus précis que celle d’un seul arbre.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPrésente un faible risque de surapprentissage\u003C/h3\u003E\n\u003Cp\u003EContrairement aux arbres décisionnels individuels qui peuvent mémoriser trop fidèlement les données d’entraînement, la forêt aléatoire offre une protection naturelle contre le surapprentissage. Chaque arbre voit des données et des caractéristiques différentes, ce qui annule les biais et les erreurs individuels et permet une meilleure généralisation lorsqu’il est confronté à de nouvelles données.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGère divers types de données\u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire fonctionne parfaitement avec des types de données mixtes, y compris des valeurs numériques (comme l’âge ou le revenu) et des variables catégorielles (comme la couleur ou la marque), sans nécessiter de prétraitement approfondi. Il s’agit donc d’un bon choix pour des jeux de données réels contenant des informations désordonnées dans plusieurs formats.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIdentifie les variables de données importantes\u003C/h3\u003E\n\u003Cp\u003EL’algorithme classe automatiquement les variables d’entrée qui ont eu le plus d’influence sur une prédiction donnée, une technique connue sous le nom d’importance des caractéristiques. Les data scientists peuvent ainsi mieux comprendre leurs données, identifier les facteurs clés et potentiellement simplifier les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-modeling/\"\u003Emodèles\u003C/a\u003E sur la base des variables les plus importantes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPerformances constantes et fiables \u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire est très résistante aux valeurs aberrantes, au bruit et aux faibles variations dans les données d’entraînement. Alors que d’autres algorithmes peuvent produire des résultats très différents en cas de variations mineures des données, la forêt aléatoire maintient des performances constantes, ce qui lui confère une grande fiabilité dans les environnements de production.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENécessite une personnalisation minimale\u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire fonctionne bien « telle quelle » avec les paramètres par défaut. Elle est donc accessible aux praticiens de tous niveaux de compétence, ce qui permet un prototypage rapide et le développement de modèles de référence.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-320e64ab1c","additionalClasses":"headline-decoration","type":"heading2","lines":["Principales limites de la forêt aléatoire"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0429172cf9","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVoici les principaux inconvénients et limites de l’utilisation du modèle de forêt aléatoire :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIl est plus difficile d’interpréter les résultats \u003C/h3\u003E\n\u003Cp\u003EContrairement à un arbre décisionnel unique où il est simple de retracer le cheminement exact de la décision, la forêt aléatoire utilise des centaines d’arbres pour parvenir à une prédiction finale. Il devient alors plus difficile d’expliquer pourquoi une prédiction spécifique a été faite, ce qui limite son utilisation dans les secteurs réglementés ou les situations qui nécessitent une prise de décision transparente.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIl nécessite plus de temps\u003C/h3\u003E\n\u003Cp\u003ELa création de centaines d’arbres prend beaucoup plus de temps que l’entraînement d’un seul modèle. À mesure que le nombre d’arbres augmente, le temps de prédiction croît proportionnellement, ce qui peut poser problème pour les applications en temps réel ou les environnements aux ressources limitées.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIl peut être peu performant en cas de déséquilibre des données\u003C/h3\u003E\n\u003Cp\u003ELorsqu’il traite des jeux de données dans lesquels une classe est beaucoup plus courante que les autres (comme le filtrage des spams, où la majorité des messages sont légitimes), le modèle de forêt aléatoire se montre peu performant pour détecter les rares exceptions lorsque la précision est primordiale.  \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIl est gourmand en mémoire\u003C/h3\u003E\n\u003Cp\u003ELa forêt aléatoire nécessite de stocker tous les arbres individuels en mémoire, ce qui peut constituer un goulot d’étranglement lorsqu’il s’agit de traiter de grands jeux de données ou de créer des forêts de centaines d’arbres. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIl a des difficultés à traiter les données désordonnées \u003C/h3\u003E\n\u003Cp\u003EBien que la forêt aléatoire soit généralement efficace pour éviter le surapprentissage, elle peut néanmoins rencontrer des difficultés lorsqu’il s’agit de traiter des données extrêmement désordonnées ou imprécises. Si les mêmes erreurs apparaissent dans toutes les données d’entraînement, l’algorithme peut commencer à considérer ces erreurs comme fiables, ce qui conduit à des prédictions moins précises lorsqu’il est confronté à de nouvelles données. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1413360324":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-86abc95ae1","additionalClasses":"headline-decoration","type":"heading2","lines":["Applications réelles de la forêt aléatoire"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-46eae3f91c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVoici quelques applications concrètes de la forêt aléatoire dans différents secteurs :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDétection des fraudes\u003C/h3\u003E\n\u003Cp\u003ELes banques, les sociétés de cartes de crédit et d’autres \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/financial-services/\"\u003Eorganismes de services financiers\u003C/a\u003E utilisent la forêt aléatoire pour identifier les transactions suspectes par l’analyse des tendances de dépenses, des lieux de transaction, des montants et du moment où elles ont lieu. L’algorithme peut rapidement repérer des comportements inhabituels, comme des achats dans des pays étrangers ou plusieurs transactions à forte valeur ajoutée sur une courte période, ce qui permet de \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Edétecter les fraudes financières\u003C/a\u003E en temps réel.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDiagnostic des maladies \u003C/h3\u003E\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003Eprofessionnels de santé\u003C/a\u003E utilisent la forêt aléatoire pour faciliter le diagnostic des maladies par l’analyse des symptômes des patients, des résultats de laboratoire, des antécédents médicaux et des informations démographiques. Par exemple, les hôpitaux l’utilisent pour prédire le risque de réadmission des patients ou pour \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/care-delivery/\"\u003Eidentifier les premiers signes d’affections\u003C/a\u003E comme le diabète ou les maladies cardiaques, sur la base de multiples indicateurs de santé.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPrévision des cours boursiers \u003C/h3\u003E\n\u003Cp\u003ELes sociétés d’investissement et les plateformes de trading utilisent la forêt aléatoire pour prévoir les fluctuations des cours boursiers en analysant les indicateurs techniques, les volumes de transactions, les opinions du marché et les données économiques. Bien que les prédictions de marché restent intrinsèquement difficiles, l’algorithme permet d’identifier des tendances sur les marchés financiers et aide les traders à prendre des décisions d’achat/de vente plus éclairées.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPrévision du taux de désabonnement\u003C/h3\u003E\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/advertising-media-entertainment/\"\u003Eservices de streaming\u003C/a\u003E, les \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/telecom/\"\u003Eopérateurs de télécommunications\u003C/a\u003E et les \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/technology/\"\u003Efournisseurs de logiciels\u003C/a\u003E utilisent la forêt aléatoire pour identifier les clients sur le point de se désabonner. L’analyse des tendances d’utilisation, de l’historique des paiements, des interactions avec le service client et des données démographiques permet aux entreprises de contacter de manière proactive les clients à risque afin de leur proposer des offres de fidélisation.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERecommandation de produits \u003C/h3\u003E\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/retail-consumer-goods/\"\u003Eretailers en ligne\u003C/a\u003E utilisent la forêt aléatoire pour alimenter leurs recommandations de produits par l’analyse de l’historique des achats, du comportement de navigation et des similitudes entre les produits. L’algorithme contribue à augmenter les ventes en suggérant des produits pertinents que les clients sont susceptibles d’acheter en fonction de schémas d’utilisateurs similaires.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EÉvaluation des risques de crédit \u003C/h3\u003E\n\u003Cp\u003ELes banques et les organismes de prêt utilisent la forêt aléatoire pour évaluer les demandes de prêt sur la base de facteurs tels que les antécédents de crédit, les revenus, la situation professionnelle et le ratio dette/revenu. Les prêteurs peuvent ainsi prendre des décisions plus précises quant à l’octroi des prêts et aux taux d’intérêt à proposer aux différents demandeurs.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_1858830768":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"solutions",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"container_115652453":{"columnClassNames":{"text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"last",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy_copy_c":{"id":"title-v2-93df3cfbfc","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusion"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy_copy_":{"id":"text-d0be824ffd","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa forêt aléatoire est un outil polyvalent et performant pour établir des prévisions. Elle offre une précision élevée et constante dans des applications qui vont de la détection des fraudes au diagnostic médical, sans oublier le filtrage des spams. L’utilisation de plusieurs arbres décisionnels permet à la forêt aléatoire d’éviter la plupart des problèmes liés aux données désordonnées et au surapprentissage, ce qui en fait une technologie fondamentale pour la création de modèles de machine learning. Sa capacité à traiter différents types de données et à fonctionner correctement sans réglages importants la rend accessible aux utilisateurs de tous niveaux. À mesure que les données deviennent de plus en plus complexes, les méthodes ensemblistes fiables telles que la forêt aléatoire resteront essentielles pour les praticiens qui cherchent à créer des systèmes d’IA hautes performances.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy_c","text_copy_copy_copy_"]},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-76cb3bc6bb","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ sur la forêt aléatoire"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-3f0974165b","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Pourquoi parle-t-on de forêt « aléatoire » ?","richText":"\u003Cp\u003ELe terme « aléatoire » découle de deux sources principales : Chaque arbre est entraîné sur un sous-jeu de données sélectionné de manière aléatoire et chaque arbre ne prend en compte qu’une poignée de facteurs aléatoires à chaque point de décision. Ce caractère aléatoire renforce la puissance de l’algorithme dans la mesure où il oblige les arbres à trouver différents schémas utiles qui se complètent mutuellement.\u003C/p\u003E\n"},{"title":"En quoi la forêt aléatoire diffère-t-elle d’un arbre décisionnel classique ?","richText":"\u003Cp\u003EConsidérez un arbre décisionnel comme une demande d’avis à une seule personne, tandis qu’une forêt aléatoire s’apparente à un sondage auprès d’une centaine de personnes, chacune apportant des informations légèrement différentes sur le problème. La combinaison de toutes leurs réponses par le biais d’un vote ou d’une moyenne permet d’obtenir une prédiction beaucoup plus fiable et précise que si l’on se fiait uniquement au jugement d’une seule personne.\u003C/p\u003E\n"},{"title":"Quand dois-je utiliser la forêt aléatoire plutôt que d’autres algorithmes ?","richText":"\u003Cp\u003ELa forêt aléatoire est un excellent point de départ lorsque vous souhaitez obtenir une grande précision sans passer beaucoup de temps à modifier les paramètres, en particulier si vous travaillez avec des types de données mixtes ou si vous avez besoin de comprendre quels sont les facteurs les plus importants. Toutefois, si vous devez expliquer exactement pourquoi chaque prédiction a été faite, vous pouvez envisager d’utiliser des algorithmes plus simples et plus interprétables.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"container_copy_copy__480869401":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-8bcc0dfaa1","additionalClasses":"headline-decoration","type":"heading2","lines":["Clients qui utilisent Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-6517d1befa","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-bbed5ab4ee",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-ef3886a853","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["IGS Energy utilise l’IA et le ML pour simplifier les prévisions et la détection des anomalies"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/fr/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire le témoignage"},"image":{"id":"image","lazyEnabled":true,"alt":"simon data logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--eea8935e-a8ba-4d6c-a42c-1a40ff93d58e/igs-energy-customer-card%25402x.jpg?quality=85&preferwebp=true","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EGrâce à Snowflake, IGS Energy utilise les données pour résoudre les cas d’usage d’IA et de ML (des modèles de prévision plus rentables à la détection plus précise des anomalies) afin d’atteindre son objectif d’un avenir durable pour tous.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-dd4c701774",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2_copy":{"id":"card-v2-25f9d466b8","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["WHOOP améliore les prévisions financières en matière d’IA et de ML tout en améliorant l’expérience de ses membres"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire le témoignage"},"image":{"id":"image","lazyEnabled":true,"alt":"penske logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3c22294a-3e45-4a55-aa6d-f48810873b7e/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EGrâce à Snowflake et Apache Iceberg, les équipes de WHOOP centralisent l’accès aux données tout en réduisant la complexité, en réduisant les coûts et en améliorant des processus stratégiques.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2_copy"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false}},":itemsOrder":["title_v2","flexible_column_cont"]},"container_copy_copy__373061683":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-2c50219a38","type":"heading2","lines":["Ressources sur le machine 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