April 2019
Intermediate to advanced
214 pages
5h 57m
German
Content preview from Merkmalskonstruktion für Machine LearningBecome 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 4
Auswirkungen der Merkmalsskalierung: von Bag-of-Words zu TF-IDF
Eine Bag-of-Words-Darstellung ist einfach zu erzeugen, aber bei Weitem nicht perfekt. Zählen wir alle Wörter als gleichwertig, werden am Ende einige Wörter übermäßig gewichtet. Erinnern wir uns an das Beispiel von Emma und dem Raben aus Kapitel 3. Wir suchen eine Darstellung des Dokuments, die die beiden Hauptfiguren hervorhebt. Das Wort »Emma« kommt viermal vor, »Rabe« bzw. »Raben« dreimal, aber »und« erscheint ganze sechs Male und »er« und »der« jeweils auch dreimal. Die Hauptfiguren stechen bei einer einfachen Häufigkeitszählung nicht hervor. Das ist ein Problem.
Es wäre auch gut, wenn Wörter wie »prächtig«, »glänzen«, »eingeschüchtert«, »zögernd« und »beherrschte« ausgewählt ...
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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.