Apprentissage Ensembliste
Course
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- Comprendre les principes de base de la construction de modèles ensemblistes, en se concentrant sur l'amélioration de la performance prédictive.
- Saisir les subtilités des modèles de bagging, boosting et stacking en tant que stratégies avancées d'apprentissage automatique.
- Acquérir des connaissances sur les modèles couramment utilisés tels que Random Forest et ExtraTrees, et leurs applications en science des données.
- Se lancer des défis avec des tâches impliquant l'application pratique de modèles ensemblistes, renforçant les capacités de prise de décision.
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There are 6 modules in this course
L'apprentissage ensembliste est une technique avancée d'apprentissage automatique qui combine plusieurs modèles pour améliorer la performance prédictive globale et la prise de décision lors de la résolution de tâches réelles.- Classificateur de BaggingPreview
- Défi : Résoudre la Tâche en Utilisant le Classificateur de BaggingPreview
- Régression par EnsachagePreview
- Défi : Résoudre la Tâche en Utilisant le Régressseur de BaggingPreview
- Forêt AléatoirePreview
- Défi : Déterminer l'Importance des Caractéristiques en Utilisant la Forêt RandomPreview
- Arbres SupplémentairesPreview
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