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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35
35
2.4 データマイニングプロセス
たかによって、データマイニングのソリューションの品質が決まることが多いので
す（そして、このことがなかなか受け入れられないこともあります）。
データを準備する際は、「リーク」について十分に警戒する必要があります［
Kaufman, S.
2012
］。収集した過去の履歴データに、目的変数の予測に使用できる変数が含まれているに
も関わらず、予測する時点では何らかの理由でそれを使用できないことがあります。この
ような状況がリークです。例えば、ある時点において
Web
サイトの訪問者が閲覧をやめる
か、別の
Web
サイトに移ってしまうことを予測するとします。このような予測を行う場合、
「閲覧を終えるまでの総閲覧ページ数」は予測に有効な変数です。しかしながら、閲覧を終
えるまでの
Web
サイトの総閲覧ページ数は、その一連の閲覧が終わるまではわかりません
［
Kohavi, R. 2000
］。言い換えると、閲覧中に予測したい目的変数あるのに、予測に必要な値
はすべての閲覧が完了しないとわからないのです。別の例として、ある顧客がたくさんの買
い物をしてくれる顧客であるか予測する問題を考えてみましょう。この場合、最終的に購入
された商品のカテゴリ（または税額）などが予測において有効な情報となります。しかしな
がら予測は買い物の最中、すなわち決済前に行いたいのです。そのため予測する段階ではこ
れらの情報を使用することはできません［
Kohavi, R., & Parekh, R. 2003
］
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