November 2023
Intermediate to advanced
576 pages
13h 30m
German
Content preview from Handbuch Data Science mit Python
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KAPITEL 48
Ausführlich: Gaußsche Mixture-Modelle
Das im letzten Kapitel vorgestellte k-Means-Modell ist einfach und relativ gut verständlich, aber eben diese Einfachheit kann bei der Anwendung zu Problemen führen. Insbesondere die nicht probabilistische Natur des k-Means-Verfahrens und die Verwendung eines simplen Abstands vom Cluster-Zentrum führen in der Praxis in vielen Fällen zu schlechten Ergebnissen. In diesem Kapitel betrachten wir das gaußsche Mixture-Modell (GMM), das man als Erweiterung der dem k-Means-Algorithmus zugrunde liegenden Idee auffassen kann, das aber auch als leistungsfähiges Werkzeug für Abschätzungen jenseits des einfachen Clusterings dient.
Wir fangen mit den Standardimporten an:
In [1]: %matplotlib inline
import matplotlib.pyplot ...
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