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
125
>>>
Y = Y=="positive"
>>>
train_model(create_ngram_model)
0.805 0.024 0.878 0.016
ここではベクトル化されたトライグラム（3-gram）
†
でのTF-IDF値についてナイーブベイズを用い
ました。結果は正解率が80.5％であり、AUCは87.8％でした。次に示される適合率- 再現率のグラフ
を見ると、前の章で見たときの図と比べて、より良い結果であることがわかるでしょう（図6-2）。
図6-2
最初にしては、とても良い結果だと言えます。感情分析を100%の精度で行うのはおそらく不可能で
あるということを考えると、この結果はより説得力を与えるものでしょう。たとえ人間であっても、あ
るツイートに対してどちらに分類するか意見がわかれることがよくあるものです。
6.4.2
全てのクラスを対象とする
これまでのところ、私たちは問題を少し単純化して考えています。なぜなら「ポジティブ」と「ネガ
ティブ」のツイートだけを対象としているからです。今の私たちの状況は、ツイートが感情を含むかど
うかということに関して、完璧に分類を行ってくれる理想的な分類器が先に存在していることを想定し
ています。その理想的な分類器が「感情あり」と分類したツイートに対して、先ほどのナイーブベイズ
分類器を用いてるのです。
それでは、どうすればツイートが感情を含むかどうかを分類することができるでしょうか？ それを
行うためには、 ...
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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.
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.