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Machine learning : les fondamentaux
book

Machine learning : les fondamentaux

by Matt Harrison
March 2019
Intermediate to advanced
256 pages
4h 57m
French
Editions First
Content preview from Machine learning : les fondamentaux

CHAPITRE 9 Classes non équilibrées

Lorsque vous classifiez des données, si les classes ne sont pas équilibrées au niveau de leur taille, les plus présentes vont influer sur le modèle. Si vous avez par exemple un cas positif pour 99 cas négatifs, vous atteignez aisément 99 % de précision en considérant tous les cas comme négatifs. Plusieurs options sont disponibles pour gérer les classes déséquilibrées.

Changement de métrique

Une solution consiste à utiliser autre chose pour le calibrage que l’exactitude (accuracy), par exemple la valeur AUC. Lorsque les tailles cibles sont différentes, la précision et le rappel constituent de meilleures options, mais il y en a d’autres.

Algorithmes arborescents et ensembles

Les performances des modèles arborescents ...

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Publisher Resources

ISBN: 9782412056028