October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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6.4
最初の分類器を作成し、調整を行う
123
●
GaussianNB
これは、特徴量は正規分布（ガウス分布）に従って分布するであろうと仮定します。これが使わ
れる問題としては、たとえば、「人の身長と体重が与えたれたときに、その人の性別を分類する」
ような問題です。私たちの場合は、ツイートが与えられて、そこから各単語についてその頻度を
抽出します。それらが正規分布に従わないのは明らかでしょう。
●
MultinomialNB
これは、ある事象が発生した回数を特徴量としていると仮定します。私たちはツイートの各単語
の出現回数を特徴量としているため、この仮定は私たちの場合と合致します。実際に、この分類
器はTF-IDFと相性が良く、上手く分類できるケースが多くあります。
●
BernoulliNB
これは
MultinomialNB
と似ています。しかし、単語の回数ではなく、単語が出現した／出現
していないの2値で特徴量が表される場合に適しています。
私たちは主に単語の出現回数を
特徴量として用いるので、
MultinomialNB
が最適であると言えま
す。
6.4.1
簡単な問題を最初に解く
実際のツイートをいくつか見ればすぐわかりますが、ツイートが「ポジティブ」か「ネガティブ」の
どちらか、というだけの分類は不適切です。実際、ほとんどのツイートには感情が含まれていないの
で、「意見なし（irrelevant）」または「感情なし（neutral）」になるでしょう（たとえば、「新書：実践
機械学習 http://. ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.