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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240
8 章 モデル性能の可視化
8.4
ROC
曲線の下の面積（
AUC
）
重要な要約統計量として、
ROC
曲線の下の面積（
AUC
）があります。名前が示している
ように、単純に、分類器が描く
ROC
曲線の下の面積の割合を、全体領域の面積で割ったも
のです。その値は、
0
から
1
の範囲を取ります。
ROC
曲線は、面積だけではなく、それ以上
の情報を提供するものですが、
AUC
は性能を
1
つの数値で評価したいときや、分類器の使
用条件以外は何もわからないようなときに便利で、よく使われます。「
8.6
例：乗り換えモ
デリングの性能分析」では、
AUC
統計量の使い方について説明します。今のところ、分類器
の予測性能を評価する汎用的な要約統計量である、という理解で十分です。
技術的な補足を 1 つしておくと、AUC は統計学でよく知られた順位検定である
Mann-Whitney-Wilcoxon 検定 と 同等です［Wilcoxon 1945］。また、ジニ 係
数
†
をわずかに代数変換したもの［Adams & Hand 1999］［Stein 2005］と
も同等です。どちらの手法も、ランダムに選んだ陽性インスタンスは、ランダムに
選んだ陰性のインスタンスよりも上位にランク付けされる確率が同等です。
8.5
累積反応とリフト曲線
ROC
曲線は一般的なツールとして、モデルの分類性能の可視化、クラス確率推定、スコア
付けに利用できます。ただし、これまで知らなかった読者の方々は、ここまでの説明を読ん
で感じたかもしれませんが、
ROC
曲線は、結果を理解するべき多くのビジネス上の関係者に ...
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