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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210
9
章 半教師あり学習
図9-2に、結果を示す。
図9-2 教師なしモデルの結果
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
0.57
だ。不正の
75%
を検出しよう
とすると、適合率はわずか
45%
になる。教師なしシステムの平均適合率は、教師ありシステムのそれ
とあまり変わらないが、再現率が
75%
の場合の適合率はかなり良い。
しかしこの教師なしシステムの性能もあまり良くはない。
9.4
半教師ありモデル
オートエンコーダで学習した表現（つまり隠れ層）と元の訓練セットを併せて用いて、勾配ブースティ
ングアルゴリズムで処理してみよう。これは教師あり学習と教師なし学習の利点を併せ持つ、半教師
あり手法になる。
隠れ層を取得するには、
Keras API
の
Model
クラスの
get_layer
関数を使う。
layer_name = 'hidden_layer'
intermediate_layer_model = Model(inputs=model.input, \
outputs=model.get_layer(layer_name).output)
intermediate_output_train = intermediate_layer_model.predict(X_train_original)
intermediate_output_test = intermediate_layer_model.predict(X_test_original)
オートエンコーダの表現の
DataFrame ...
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