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de décision en machine learning : guide complet"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-eb6b219839","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EDécouvrez les arbres de décision : fonctionnement, Explorez les types d'arbres de décision, l'analyse, les exemples et les bonnes pratiques pour le machine learning et la planification.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip":"aem-GridColumn 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip"],":type":"snowflake-site/components/container"}},":itemsOrder":["title_v2","text","container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-8287f3ab2d",":items":{"youtube":{"id":"embed-7a1be46bdd","youtubeVideoId":"F34xlRoQ3eQ","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-c7f742c315","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-9e83c29b84",":items":{"text_894059747":{"id":"text-5894c9573e","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003ESujets liés aux modèles de ML :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-ee171d9c8c","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/linear-regression/\"\u003ERégression linéaire\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/random-forest/\"\u003EForêt aléatoire\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/regression/\"\u003EModèle de régression\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/support-vector-machine/\"\u003ESupport Vector Machine (SVM)\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-d44c00d0cd","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-694c8d782a","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDÉFINITION DES ARBRES DE DÉCISION\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EUn arbre de décision en machine learning est un modèle prédictif qui décompose un problème en une séquence de questions fondées sur les données, guidant chaque observation vers une prédiction de classification ou de régression. Sa structure lisible permet d'auditer et d'interpréter le raisonnement du modèle.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-be3baa9d52","text":"\u003Cp\u003ETout comme les humains examinent différentes options avant de prendre une décision, les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/\"\u003Emodèles de machine learning\u003C/a\u003E utilisent plusieurs méthodes pour faire une prédiction ou une recommandation. Les arbres de décision sont une option populaire en \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) car ils décomposent les problèmes en étapes simples, rendant les résultats simples à comprendre.\u003C/p\u003E\n\u003Cp\u003ELes arbres de décision sont couramment utilisés en apprentissage supervisé, où les modèles s’appuient sur des exemples qui ont déjà des réponses connues et correctes.  Ils traitent couramment des tâches de classification, comme l'identification des spams, et des tâches de régression, comme la prévision de la consommation énergétique d'un bâtiment.  Ce qui les distingue, c'est la manière dont le raisonnement de l'arbre de décision peut être visualisé et interprété. En observant comment une « branche » se divise dans différentes directions, en traitant diverses questions fondées sur les données, on comprend clairement comment le raisonnement du modèle a conduit à un certain résultat.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-decision-tree":{"id":"title-v2-b54168402e","additionalClasses":"anchor-title anchor-title--what-is-a-decision-tree","type":"heading2","lines":["Définition d’un arbre de décision"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-decision-tree_0":{"id":"text-f5bd89e259","text":"\u003Cp\u003EUn arbre de décision est un modèle prédictif qui aboutit à une réponse en posant une séquence de questions simples sur les données, réduisant les possibilités à chaque étape jusqu'à ce qu'un seul résultat subsiste.  Sa caractéristique déterminante est la lisibilité : le chemin menant à une prédiction donnée peut être relu comme une chaîne de règles simples, ce qui en fait l'un des rares modèles qu'un non-spécialiste peut auditer sans assistance.\u003C/p\u003E\n\u003Cp\u003ELes arbres de décision fonctionnent comme des organigrammes. Chaque intersection représente un point de décision menant à des résultats différents. Ainsi, les utilisateurs et les ordinateurs peuvent facilement envisager les différentes options, considérer les possibilités et comprendre les résultats.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-does-a-decision-tree-work":{"id":"title-v2-effb2445c9","additionalClasses":"anchor-title anchor-title--how-does-a-decision-tree-work","type":"heading2","lines":["Fonctionnement d’un arbre de décision"],":type":"snowflake-site/components/title-v2"},"text_how_does_a_deci":{"id":"text-9628a58ec9","text":"\u003Cp\u003EUn arbre de décision décompose un problème en une série de questions. Chaque question contribue à réduire l’incertitude, jusqu’à ce que la réponse devienne claire.\u003C/p\u003E\n\u003Cp\u003ELe processus commence à la racine avec une question basée sur les données. À cette étape, l’algorithme effectue la sélection d’une caractéristique, c’est‑à‑dire l’identification de la variable la plus pertinente pour diviser les données. Chaque réponse mène à une autre question, toujours en fonction de la caractéristique qui permet la meilleure division des données à ce stade. L’arbre poursuit ce processus jusqu’à atteindre un nœud feuille, où une prédiction ou une décision finale est formulée.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-essentials":{"id":"title-v2-aec0734b72","additionalClasses":"anchor-title anchor-title--decision-tree-essentials","type":"heading2","lines":["Principes fondamentaux des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-essentials_0":{"id":"text-af9f5ff8d4","text":"\u003Cp\u003ELes arbres de décision comptent généralement quatre composants :\u003C/p\u003E\n\u003Ch3\u003ENœuds racines\u003C/h3\u003E\n\u003Cp\u003EComme les arbres physiques, les nœuds racines sont là où tout commence.  Ils constituent la première étape du processus de raisonnement, où l'ensemble des données relatives à une ou plusieurs questions se rassemble avant que la moindre division ne soit effectuée.\u003C/p\u003E\n\u003Ch3\u003EBranches\u003C/h3\u003E\n\u003Cp\u003ELes branches divisent les jeux de données en fonction de valeurs présentes dans les données.  Par exemple, les clients âgés de plus de 30 ans peuvent bifurquer sur une branche, ceux de moins de 30 ans sur une autre. L’arbre de décision guide chaque groupe vers son propre résultat.\u003C/p\u003E\n\u003Ch3\u003ENœuds internes\u003C/h3\u003E\n\u003Cp\u003ELes nœuds internes sont des points de décision où le modèle pose une question sur les données afin de l'orienter vers un chemin particulier. Par exemple, le modèle de service d'un détaillant pourrait examiner l'historique d'achat et se demander : « Le client A a-t-il tendance à acheter des chemises rouges ou bleues ? »\u003C/p\u003E\n\u003Ch3\u003ENœuds feuilles\u003C/h3\u003E\n\u003Cp\u003ELes nœuds feuilles sont les points terminaux d'un arbre de décision, où le processus de raisonnement s'arrête et où le modèle fournit un résultat. Pour poursuivre l'exemple du commerce de détail, si l'historique du client indique une préférence pour les chemises rouges, l'arbre de décision du modèle d'apprentissage automatique peut aboutir à un nœud feuille qui incite le modèle à recommander plusieurs nouvelles options de chemises rouges à acheter.\u003C/p\u003E\n\u003Cp\u003E En pratique, les arbres de décision explorent plusieurs chemins à la fois.  Leur logique se divise dans différentes directions afin de trouver la meilleure réponse. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text":{"id":"text-b664a50081","text":"\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 968 1046\" width=\"100%\" role=\"img\" aria-labelledby=\"dt-title dt-desc\" style=\"display:block;max-width:760px;margin:2rem auto;height:auto;\"\u003E\u003Ctitle id=\"dt-title\"\u003EAnatomie d'un arbre de décision\u003C/title\u003E\u003Cdesc id=\"dt-desc\"\u003EUn arbre de décision pour une recommandation dans le retail. Le nœud racine contient tout l'historique d'achat de l'acheteur. Il se ramifie selon que l'acheteur a plus de 30 ans ou non. 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font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#29B5E8\"\u003ENŒUD RACINE\u003C/text\u003E\u003Ctext x=\"32\" y=\"103\" font-family=\"Lato, Arial, sans-serif\" font-size=\"38\" font-weight=\"700\" fill=\"#FFFFFF\"\u003ETout l'historique d'achat de l'acheteur\u003C/text\u003E\u003Crect x=\"110\" y=\"210\" width=\"858\" height=\"126\" rx=\"12\" fill=\"#29B5E8\"/\u003E\u003Ctext x=\"142\" y=\"258\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#11253F\"\u003ENŒUD INTERNE\u003C/text\u003E\u003Ctext x=\"142\" y=\"306\" font-family=\"Lato, Arial, sans-serif\" font-size=\"36\" font-weight=\"700\" fill=\"#FFFFFF\"\u003EA déjà acheté des chemises rouges ?\u003C/text\u003E\u003Crect x=\"110\" y=\"408\" width=\"414\" height=\"140\" rx=\"12\" fill=\"#C4E4F7\"/\u003E\u003Ctext x=\"138\" y=\"458\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" 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font-size=\"30\" font-weight=\"700\" fill=\"#11253F\"\u003ERecommander du bleu tendance\u003C/text\u003E\u003Cg fill=\"none\" stroke=\"#5B9FD4\" stroke-width=\"4\" stroke-linecap=\"round\"\u003E\u003Cpath d=\"M0 1016 H56\"/\u003E\u003C/g\u003E\u003Ctext x=\"70\" y=\"1026\" font-family=\"Texta, 'Texta Bold', Arial, sans-serif\" font-size=\"27\" font-weight=\"800\" letter-spacing=\"0\" fill=\"#11567F\"\u003EBRANCHE\u003C/text\u003E\u003Ctext x=\"196\" y=\"1026\" font-family=\"Lato, Arial, sans-serif\" font-size=\"27\" font-style=\"italic\" fill=\"#6B7680\"\u003Edivise le jeu de données sur une valeur\u003C/text\u003E\u003C/svg\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_types-of-decision-trees":{"id":"title-v2-297f6bfc7a","additionalClasses":"anchor-title anchor-title--types-of-decision-trees","type":"heading2","lines":["Types d’arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_types-of-decision-trees_0":{"id":"text-2d988e159d","text":"\u003Cp\u003EDe nombreux algorithmes courants d’arbre de décision sont disponibles. La plupart peuvent être appliqués à des tâches de classification et de régression. Elles incluent :\u003C/p\u003E\n\u003Ch3\u003ECART (arbres de classification et de régression)\u003C/h3\u003E\n\u003Cp\u003E Largement utilisé, l'algorithme CART se distingue des autres méthodes d'arbres de décision par le fait qu'il crée toujours des divisions binaires (oui/non) pour chaque caractéristique, en se concentrant sur la division qui sépare le mieux les valeurs des données. Par exemple, un modèle CART prédisant si un prêt doit être approuvé pourrait d'abord diviser les demandeurs selon le critère « revenu \u003E 50 000 $ » (oui/non), puis continuer à diviser chaque groupe en fonction d'autres facteurs, comme le fait que le score de crédit du demandeur soit supérieur à 750 ou qu'il soit employé.  C'est l'algorithme derrière la plupart des implémentations d'arbres de production, y compris \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/api/modeling/snowflake.ml.modeling.tree.DecisionTreeClassifier\"\u003EDecisionTreeClassifier dans Snowflake ML\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EID3 (Iterative Dichotomiser 3)\u003C/h3\u003E\n\u003Cp\u003EID3, l’un des tout premiers algorithmes d’arbre de décision populaires, divise les données en groupes plus petits en choisissant des questions qui réduisent les réponses possibles, jusqu’à obtenir une prédiction ou une recommandation souhaitée. Par exemple, un filtre anti‑spam peut identifier les e‑mails avec le mot « offre », car ce terme est couramment utilisé dans les publicités commerciales.\u003C/p\u003E\n\u003Ch3\u003EC4.5\u003C/h3\u003E\n\u003Cp\u003EC4.5 construit des arbres de décision en posant une série de questions oui/non qui divisent les données en groupes plus restreints, ce qui facilite l'obtention de prédictions plus précises. Il améliore l'algorithme ID3 en traitant à la fois les valeurs catégorielles (comme « spam » ou « non spam ») et les valeurs numériques (comme « l'âge » ou « le revenu »), tout en contournant les lacunes telles que les données manquantes.  Par exemple, une entreprise de télécommunications pourrait utiliser C4.5 pour pondérer des facteurs tels que l'âge, la localisation et l'utilisation des données afin d'établir des options de forfait spécifiques pour un client, même avec des informations incomplètes sur le prospect.\u003C/p\u003E\n\u003Ch3\u003ECHAID (Chi‑Square Automatic Interaction Detection)\u003C/h3\u003E\n\u003Cp\u003EL’algorithme CHAID utilise des tests statistiques pour décider où diviser des données, ce qui crée souvent des branches avec plusieurs options à la fois. Par exemple, un retailer pourrait y recourir pour classer ses clients par tranches d’âge (adolescents, jeunes adultes, personnes d’âge moyen et seniors), afin de prédire quel groupe démographique est le plus susceptible d’adopter un nouveau programme de fidélité.\u003C/p\u003E\n\u003Ch3\u003EArbres d’inférence conditionnelle\u003C/h3\u003E\n\u003Cp\u003ELes arbres d’inférence conditionnelle réduisent les biais en testant si une variable est suffisamment pertinente pour justifier une division. Ils diffèrent en cela des arbres de décision classiques comme ID3 et CART, qui divisent les données pas‑à‑pas, sans vérifier si un facteur est statistiquement significatif. Par exemple, un arbre de décision classique pourrait privilégier « l'université fréquentée », tandis qu'un arbre d'inférence conditionnelle pourrait l'écarter, la jugeant statistiquement non pertinente pour prédire la performance professionnelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-splitting-criteria":{"id":"title-v2-89457a1a78","additionalClasses":"anchor-title anchor-title--decision-tree-splitting-criteria","type":"heading2","lines":["Critères de division des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-splitting-criteria_0":{"id":"text-cfd5ecbcc5","text":"\u003Cp\u003ELors de la séparation des données, les modèles de ML utilisent généralement l’un de ces deux critères courants d’arbre de décision : l’impureté de Gini ou l’entropie. Chacun mesure le degré de mixité des données, puis l’algorithme applique la méthode qu’il a choisie pour trouver la séparation la plus efficace entre les données.\u003C/p\u003E\n\u003Ch3\u003EImpureté de Gini\u003C/h3\u003E\n\u003Cp\u003EL’impureté de Gini examine si une question divise les données en groupes clairs. Mathématiquement, cela reflète la probabilité qu'un élément aléatoire soit mal classé s'il était étiqueté selon la distribution du groupe. L’algorithme CART applique cette mesure pour tester différentes séparations et choisit celle qui produit la séparation la plus propre. Par exemple, on peut demander à des personnes si elles sont fatiguées afin de créer deux groupes : celles qui sont susceptibles de boire du café et celles qui ne le sont pas.\u003C/p\u003E\n\u003Ch3\u003EEntropie\u003C/h3\u003E\n\u003Cp\u003EL'entropie mesure l'incertitude du jeu de données. Des algorithmes (comme ID3 et C4.5) s’appuient sur l’entropie pour calculer le gain d’information, qui représente la réduction de l’incertitude résultant d’une division. L’arbre sélectionne la division qui réduit le plus l’incertitude, afin de créer la séparation la plus claire entre les classes. Pour reprendre l’exemple du café, la question « est‑ce le matin ou l’après‑midi ? » réduit l’incertitude, car elle sépare les personnes en groupes plus clairs pour guider la décision.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-decision-trees-used-for":{"id":"title-v2-a4569deffa","additionalClasses":"anchor-title anchor-title--what-are-decision-trees-used-for","type":"heading2","lines":["Cas d’usage des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_what-are-decision-trees-used-for_0":{"id":"text-9db5b28113","text":"\u003Cp\u003EEn machine learning, les arbres de décision aident les modèles à transformer des données brutes en informations exploitables. Cela est particulièrement utile dans les secteurs où les décisions doivent être bien étayées et fiables.\u003C/p\u003E\n\u003Cp\u003EVoici quelques utilisations courantes des arbres de décision :\u003C/p\u003E\n\u003Ch3\u003EStratégie et planification commerciales\u003C/h3\u003E\n\u003Cp\u003ELes modèles d'apprentissage automatique entraînés à l'aide d'arbres de décision sont utiles pour prévoir des éléments tels que la croissance des ventes, les tendances tarifaires, l'attrition client, ainsi que la demande de la chaîne d'approvisionnement et les niveaux de stock. \u003C/p\u003E\n\u003Ch3\u003EÉvaluation et atténuation des risques\u003C/h3\u003E\n\u003Cp\u003EDans le secteur de la finance et des assurances, des arbres de décision aident à évaluer certains risques, comme les défauts de paiement, les sinistres ou d’autres pertes. En suivant les branches issues des données clients (antécédents de crédit, niveaux de revenus, tendances concernant les sinistres, etc.), ils aident les actuaires, les souscripteurs et les analystes financiers à estimer plus précisément les risques. Pour un exemple, voir \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-predicting-insurance-claims-regression-model/\"\u003Ela prédiction des sinistres d'assurance avec un modèle de régression XGBoost\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003ESegmentation et ciblage des clients\u003C/h3\u003E\n\u003Cp\u003EDes spécialistes du marketing peuvent utiliser des modèles d’arbres de décision pour segmenter leurs \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/customer-segmentation/\"\u003Eclients\u003C/a\u003E en groupes en fonction de leurs comportements d’achat, de leurs caractéristiques démographiques et de leur activité en ligne. Les entreprises peuvent ainsi proposer des offres plus personnalisées et prédire quels clients sont les plus susceptibles de répondre à leurs campagnes. Snowflake publie un \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-e2e-customer-targeting-with-snowflake-ml/\"\u003Eguide de ciblage client de bout en bout\u003C/a\u003E qui suit ce schéma.\u003C/p\u003E\n\u003Ch3\u003EDiagnostics et traitements médicaux\u003C/h3\u003E\n\u003Cp\u003EDans le secteur de la santé, les modèles de ML s’appuient souvent sur des arbres de décision pour interpréter des données patients. Par exemple, un modèle peut pondérer des symptômes, analyser des résultats de tests et examiner des antécédents familiaux afin de recueillir des informations essentielles pour guider les diagnostics et les traitements. Un \u003Ca href=\"https://www.snowflake.com/en/developers/guides/healthcare-ml-breast-cancer-classification/\"\u003Eguide de classification du cancer du sein conçu avec XGBoost\u003C/a\u003E montre comment un ensemble basé sur des arbres est appliqué exactement à ce type de données de diagnostic.\u003C/p\u003E\n\u003Ch3\u003EDétection des fraudes financières\u003C/h3\u003E\n\u003Cp\u003EDes banques et d’autres institutions financières réticentes au risque peuvent utiliser des modèles d’arbres de décision pour détecter les activités suspectes. En analysant des tendances (montants d’achat, retours, etc.), des modèles peuvent identifier des transactions susceptibles de trahir une fraude, un blanchiment d’argent ou d’autres activités potentiellement criminelles. La \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Esolution de détection des fraudes et des crimes financiers\u003C/a\u003E de Snowflake explique comment faire évoluer ces modèles sur des données de transaction.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_advantages-of-decision-trees":{"id":"title-v2-b563f6cefc","additionalClasses":"anchor-title anchor-title--advantages-of-decision-trees","type":"heading2","lines":["Avantages des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_advantages-of-decision-trees_0":{"id":"text-27689b5309","text":"\u003Cp\u003ELes arbres de décision simplifient et accélèrent des processus de raisonnement chronophages et fournissent des résultats plus rapidement et plus efficacement. Voici quelques‑uns de leurs avantages spécifiques :\u003C/p\u003E\n\u003Ch3\u003EInterprétation simple\u003C/h3\u003E\n\u003Cp\u003ELa transparence des arbres de décision aide à démystifier le processus de raisonnement du ML. Tout le monde peut suivre visuellement la logique pas‑à‑pas qui a mené le modèle à ses conclusions et recommandations.\u003C/p\u003E\n\u003Cp\u003EParce qu'un arbre entraîné peut être lu comme un ensemble de règles lisibles par l'homme, il est souvent choisi comme l'alternative interprétable à des modèles plus précis mais opaques tels que les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/neural-network/\"\u003Eréseaux de neurones\u003C/a\u003E. Dans les environnements réglementés où une décision doit être expliquée plutôt que simplement justifiée par sa précision, cette propriété peut avoir plus d'importance que quelques points d'amélioration prédictive, c'est pourquoi les arbres restent courants partout où la transparence de l'\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-transparency/\"\u003EIA\u003C/a\u003E est une exigence.\u003C/p\u003E\n\u003Ch3\u003ECharge allégée de préparation des données\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent gérer à la fois des catégories et des valeurs numériques, de sorte que les analystes n'ont pas à passer autant de temps à convertir ou à reformater les données. Le travail de préparation préalable nécessaire avant d’exécuter les modèles est donc réduit.\u003C/p\u003E\n\u003Ch3\u003EGrande flexibilité\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent s’adapter à divers problèmes, car chacun d’entre eux constitue un modèle autonome capable de formuler des prédictions de manière indépendante. De plus, cette conception flexible permet de combiner de nombreux arbres et d’agréger leurs résultats, afin d’effectuer des tâches plus importantes et plus complexes.\u003C/p\u003E\n\u003Ch3\u003EGestion des valeurs manquantes\u003C/h3\u003E\n\u003Cp\u003EContrairement à certains modèles qui nécessitent des jeux de données complets, les arbres de décision peuvent fonctionner lorsque des informations sont manquantes. Pour ce faire, ils attribuent des pondérations plus faibles aux enregistrements incomplets ou divisent les données entre plusieurs branches possibles.\u003C/p\u003E\n\u003Ch3\u003EFonctionne bien avec de petits jeux de données\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent identifier des régularités utiles sans grandes quantités de données. Ils sont efficaces même lorsque les informations sont limitées, ce qui les rend précieux dans les domaines où les données sont trop rares ou chronophages à recueillir.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_decision-tree-limitations":{"id":"title-v2-223c122cf8","additionalClasses":"anchor-title anchor-title--decision-tree-limitations","type":"heading2","lines":["Limites des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-limitations_0":{"id":"text-4d70c13509","text":"\u003Cp\u003EMalgré leurs avantages, les arbres de décision présentent malgré tout des inconvénients. Voici quelques problèmes courants rencontrés par les utilisateurs et les machines qui s’appuient sur des arbres de décision :\u003C/p\u003E\n\u003Ch3\u003ETendance au surapprentissage\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent aller trop loin dans le détail, en s’attardant sur des anomalies dans les données d’entraînement au lieu d’apprendre des schémas généraux. Par conséquent, un modèle peut sembler précis pendant l’entraînement, mais avoir du mal à gérer de nouvelles données qu’il n’a jamais vues.\u003C/p\u003E\n\u003Ch3\u003ESensible aux données « bruitées »\u003C/h3\u003E\n\u003Cp\u003ELes arbres de décision peuvent être perturbés par des variations aléatoires ou non pertinentes dans un jeu de données qui ne reflètent pas de véritables schémas. Ainsi, le moindre bruit peut pousser un arbre à prendre de mauvaises décisions, ce qui le fait aboutir sur des prédictions instables.\u003C/p\u003E\n\u003Ch3\u003ERisque de biais\u003C/h3\u003E\n\u003Cp\u003ESi une caractéristique particulière domine un jeu de données, un arbre de décision peut parfois la sur-indexer au détriment d'autres facteurs tout aussi ou plus importants. Par exemple, si un modèle médical accorde plus d'importance au code postal d'un patient qu'à des facteurs tels que l'alimentation ou le mode de vie, cela peut conduire à des prédictions, des recommandations et des diagnostics inexacts.\u003C/p\u003E\n\u003Ch3\u003EPrécision inférieure à celle des méthodes d’ensemble\u003C/h3\u003E\n\u003Cp\u003EDes arbres de décision uniques peuvent prendre des décisions seuls, ce qui peut provoquer des erreurs ou un surapprentissage. Les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/ensemble-learning/\"\u003Eméthodes d'ensemble\u003C/a\u003E, en revanche, combinent les résultats de plusieurs arbres. Cette approche collective fournit généralement des résultats plus précis, plus complets et plus constants.\u003C/p\u003E\n\u003Cp\u003EL'arbre de décision est l'apprenant de base dans les familles d'ensembles les plus connues, de sorte que les deux approches ne sont pas vraiment rivales. Une \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/random-forest/\"\u003Eforêt aléatoire\u003C/a\u003E entraîne de nombreux arbres en parallèle sur différents sous-ensembles des données et des caractéristiques, puis fait la moyenne de leurs sorties pour annuler la variance de n'importe quel arbre unique. Le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/what-is-gradient-boosting/\"\u003Egradient boosting\u003C/a\u003E utilise le même apprenant de base mais construit les arbres de manière séquentielle, chaque nouvel arbre corrigeant les erreurs laissées par les précédents. La structure d'ensemble diffère ; l'arbre sous-jacent, non.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_decision-tree-limitations_0":{"id":"text-331a5b7b71","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EPIÈGE COURANT\u003C/b\u003E \u003C/p\u003E\n\u003Cp\u003ELe surapprentissage est le mode de défaillance à l'origine de la plupart des limitations évoquées ci-dessus, un arbre non élagué qui a mémorisé les particularités des données d'entraînement paraîtra également anormalement sensible au bruit et anormalement confiant à l'égard d'une caractéristique dominante. Considérez l'élagage et la validation sur des données inédites comme une pratique standard, plutôt que comme un réglage optionnel. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_decision-tree-best-practices":{"id":"title-v2-da0186b379","additionalClasses":"anchor-title anchor-title--decision-tree-best-practices","type":"heading2","lines":["Meilleures pratiques autour des arbres de décision"],":type":"snowflake-site/components/title-v2"},"text_decision-tree-best-practices_0":{"id":"text-f86ad9bcdd","text":"\u003Cp\u003ELes entreprises peuvent optimiser l’efficacité de leurs arbres de décision de ML en suivant ces conseils pratiques :\u003C/p\u003E\n\u003Ch3\u003ESélectionnez des caractéristiques pertinentes\u003C/h3\u003E\n\u003Cp\u003EDonnez la priorité aux facteurs qui optimisent la séparation des données, comme les montants des transactions pour la détection des fraudes ou les résultats des tests pour les diagnostics médicaux. Les caractéristiques pertinentes du point de vue des prédictions peuvent aider les arbres de décision à obtenir des résultats plus clairs et à éviter des divisions inutiles. Gérer ces caractéristiques dans un \u003Ca href=\"https://www.snowflake.com/en/developers/guides/intro-to-feature-store/\"\u003Efeature store\u003C/a\u003E permet de conserver des définitions cohérentes entre l'entraînement et la production.\u003C/p\u003E\n\u003Ch3\u003ETaillez vos arbres pour éviter le surapprentissage\u003C/h3\u003E\n\u003Cp\u003ETout comme un arboriculteur taille les branches pour gérer la croissance d'un arbre et éliminer les feuilles mortes, il est important de couper les branches inutiles de l'arbre de décision. Ce nettoyage est essentiel pour empêcher un arbre de se focaliser sur des données d’entraînement et pour l’obliger plutôt à rechercher des tendances susceptibles de conduire à des résultats significatifs.\u003C/p\u003E\n\u003Ch3\u003EComparez les résultats à des données récentes\u003C/h3\u003E\n\u003Cp\u003EPour s'assurer qu'un arbre reste fiable, vérifiez ses performances en l'exposant à des données inédites.  Vous pourrez ainsi éviter le phénomène de surapprentissage. Retenir une division de test et examiner les résultats par rapport aux métriques standard d'évaluation de modèle est la méthode habituelle pour confirmer qu'un arbre généralise plutôt qu'il ne mémorise.\u003C/p\u003E\n\u003Ch3\u003ESurveillez les divisions\u003C/h3\u003E\n\u003Cp\u003EDe nombreuses bibliothèques de ML fournissent des outils pour classer les caractéristiques sur lesquelles les arbres s’appuient le plus et pour montrer comment les divisions sont effectuées. Ces contrôles permettent de comprendre plus facilement comment un modèle traite les données, raisonne et fournit ses résultats. Une fois qu'un arbre est en production, la même visibilité appartient à la surveillance de modèle continue, de sorte qu'un changement dans les caractéristiques qui dirigent les divisions soit détecté avant qu'il n'apparaisse dans les prédictions.\u003C/p\u003E\n\u003Cp\u003ELa simplicité, la clarté et la flexibilité des arbres de décision expliquent leur popularité en machine learning. Ils sont utiles pour de nombreuses tâches stratégiques : évaluation du risque de crédit, prévision des ventes, segmentation client à des fins marketing, etc. Alors que de plus en plus d’entreprises cherchent des outils d’IA et de ML fiables, les arbres de décision continueront de leur offrir une approche utile pour formuler des prédictions et des recommandations.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_decision-tree-best-practices_0":{"id":"text-c0ed5860bb","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EÀ RETENIR\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELes arbres de décision sont des modèles d'apprentissage automatique interprétables et flexibles qui décomposent des problèmes complexes de prédiction et de classification en une série de décisions simples fondées sur les données. 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