November 2023
Intermediate to advanced
576 pages
13h 30m
German
Content preview from Handbuch Data Science mit PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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KAPITEL 49
Ausführlich: Kerndichteschätzung
In Kapitel 48 haben wir uns mit gaußschen Mixture-Modellen (GMM) befasst, die so etwas wie Hybride aus Clustering- und Density-Estimator (»Dichteschätzer«) sind. Wie Sie wissen, ist ein Density-Estimator ein Algorithmus, der eine D-dimensionale Datenmenge entgegennimmt und eine Abschätzung der D-dimensionalen Wahrscheinlichkeitsverteilung der Daten liefert, aus der die Datenmenge stammt. Der GMM-Algorithmus erreicht das durch die Repräsentierung der Dichte als gewichtete Summe von Gaußverteilungen. Die Kerndichteschätzung (engl. Kernel Density Estimation, kurz KDE) ist ein Algorithmus, der die dem GMM zugrunde liegende Idee gewissermaßen konsequent zu Ende führt: Er verwendet eine Mischung, die für ...
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