November 2023
Intermediate to advanced
576 pages
13h 30m
German
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KAPITEL 44
Ausführlich: Entscheidungsbäume und Random Forests
Wir haben uns umfassend mit einem einfachen generativen Klassifikator (siehe Kapitel 41) und einem leistungsfähigen diskriminativen Klassifikator (siehe Kapitel 43) befasst. Nun betrachten wir die Gründe für einen weiteren leistungsfähigen Algorithmus – einen nichtparametrisierten Algorithmus namens Random Forests (»Zufallswälder«). Random Forests gehören zu den sogenannten Ensemblemethoden – Verfahren, die darauf beruhen, die Ergebnisse von einem Ensemble einfacherer Estimators zusammenzutragen. Das etwas Überraschende an Ensemblemethoden ist, dass das Ergebnis mehr ist als die Summe der Teile. Beispielsweise kann die Vorhersagegenauigkeit durch die Mehrheitsentscheidung einer Reihe ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.