October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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148
7
章 回帰：レコメンド
うになりました。さらに二つを合わせたElastic netも気兼ねなく用いることができます。
7.2.2
scikit-learn
の
Lasso
と
Elastic net
を使用する
それでは、Elastic netを使ってみたいと思います。scikit-learnを用いることで、先ほどの最小二
乗法による回帰をElastic netによる回帰に簡単に差し替えることができます。
from sklearn.linear_model import ElasticNet
en = ElasticNet(fit_intercept=True, alpha=0.5)
先ほどは
lr
という名前の変数を用いましたが、ここでは
en
という変数名を用います。必要な変更
は上のコードだけです。結果は私たちの想定した通りになります。訓練誤差は5.0（前は4.6）に増加し
たのに対して、交差検定を用いた誤差は5.4（前は5.6）に減少しま
した。訓練誤差は大きくなりました
が、汎化能力を高くすることができました。ちなみに、L1罰則項を追加するためには
Lasso
クラス
を、L2罰則項を追加するためには
Ridge
クラスを用います。
図7-3は、罰則項を用いない場合の回帰（点線）とLasso回帰の場合（実線）を示します。Lasso回
帰のほうが、直線が水平に近くなっていることがわかります。まだ、Lasso回帰の恩恵が明確ではあり
ませんが、入力変数が多い場合、より顕著にその恩恵がわかることでしょう。それでは、入力
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