Overfitting: Wenn dein Modell perfekt aussieht – und trotzdem nutzlos ist Ein Modell kann die Trainingsdaten nahezu fehlerfrei erklären und trotzdem in der Praxis komplett versagen. Das nennt man Overfitting. Das Problem: Das Modell lernt nicht das zugrunde liegende Muster, sondern den Zufall, das Rauschen, die Besonderheiten der Stichprobe. Typische Folgen: • beeindruckende Fit-Kennzahlen • schlechte Vorhersagen auf neuen Daten • falsches Vertrauen in scheinbar „perfekte“ Modelle Overfitting is
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