d’erreur sur les nouveaux exemples est appelé erreur de généralisation (ou erreur hors
échantillon), et en évaluant votre modèle sur le jeu de test, vous obtenez une estima-
tion de cette erreur : celle-ci vous indique comment votre modèle se comportera sur
des cas qu’il n’a jamais rencontrés auparavant.
Si l’erreur d’apprentissage est faible (c’est-à-dire si votre modèle fait peu d’erreurs
sur le jeu d’entraînement) mais si l’erreur de généralisation est élevée, cela signie
que votre modèle surajuste les données d’entraînement.
On utilise communément 80 % des données ...
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