April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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212
9
章 半教師あり学習
図9-3 半教師ありモデルの結果
テストセットに対する適合率
-
再現率曲線に基づく平均適合率は
0.78
だ。この値は教師ありモデルよ
りも教師なしモデルよりもかなり高い。
不正の
75%
を検出するための適合率は
92%
になる。これはかなり大きな改善だ。このレベルの適合
率があれば、支払い処理担当者も安心して、システムが不正かもしれないと判断されたトランザクショ
ンを拒否することができる。
9.5
教師あり学習と教師なし学習の威力
この半教師ありクレジットカード不正検出システムでは、教師あり学習と教師なし学習の双方が重要
な役割を果たしている。最終的な勾配ブースティングモデルにおいて、どの特徴量が最も重要だとさ
れているかを見てみよう。
訓練したモデルから特徴量の重要度を取り出してみよう。
featuresImportance = pd.DataFrame(data=list(gbm.feature_importance()), \
index=X_train.columns,columns=['featImportance'])
featuresImportance = featuresImportance/featuresImportance.sum()
featuresImportance.sort_values(by='featImportance', \
ascending=False,inplace=True)
featuresImportance
表9-1に、最も重要な特
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