July 2014
Beginner to intermediate
456 pages
11h 26m
Japanese
Content preview from 戦略的データサイエンス入門 ―ビジネスに活かすコンセプトとテクニック
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291
291
10.6 Bag-of-Words を超えて
用語
t
に関するドキュメント集合について再度考えてみましょう。それぞれのドキュメン
トは確率
p
で
t
が出現するか、確率
1
−
p
で
t
が出現しないかのどちらかです。
t
が出現しな
いすべてのドキュメントに出現する、疑似的な用語
not_t
を
t
の鏡像として定義してみましょ
う。この新しい用語の
IDF
はどのようになるでしょうか。次のように表すことができます。
IDF(not_T )
＝
log1/(1
−
p)
＝−
log(1
−
p)
図
10-5
の左上のグラフを見てください。
IDF(t)
と
IDF(not_t)
の
2
つのグラフは、期待通
り互いに鏡像となっています。ここで、式
3-1
で示されたエントロピーの定義を思い出して
ください。
p
2
＝
1
−
p
1
で表される
2
つの変数を用いたエントロピーは、次のようになります。
エントロピー＝−
p
1
log( p
1
)
−
p
2
log( p
2
)
これまで見てきたケースでは、ドキュメント内に確率
p
で出現する、もしくは確率
1
−
p
で出現しない用語
t
を対象としています。よって、
t
によって分割された集合のエントロピー
は、次の定義に帰着します。
エントロピー
(t )
＝−
p log( p)
−
(1
−
p) log(1
−
p)
それでは、ここまで見てきた
IDF(t)
と
IDF(not_t)
の定義から、エントロピーをさらに展
開、単純化してみましょう（参考までに、ここで展開したさまざまな部分式は図
10-5
の右
上のグラフにプロットされています）。
エントロピー
(t )
＝−
p log( ...
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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.