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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104
5
章 クラス分類：悪い回答を判別する
5.6.2
ロジスティック回帰を今回の問題に適用する
先ほど見た例は、ロジスティック回帰の美しさを見てもらうために用意したものです。それでは、ノ
イズが含まれるこれまで扱ってきたデータに対してはどのような結果になるのでしょうか？
これまでのデータに対して、ロジスティック回帰を用いた場合の正解率の平均値と標準偏差を算出
します。結果は表5-5のようになります。最近傍法で一番良い結果（
k=90
）のときと比較すると、それ
よりも少しだけ良くなっていることがわかりますが、それほど良くなっているとは言えません。
表5-5 平均値と標準偏差の算出結果
手法 平均値 標準偏差
LogReg C=0.1
0.6310 0.02791
LogReg C=100.00
0.6300 0.03170
LogReg C=10.00
0.6300 0.03170
LogReg C=0.01
0.6295 0.02752
LogReg C=1.00
0.6290 0.03270
90NN
0.6280 0.02777
表5-5における「
LogReg C=0.1
」などの“
C
”は、ロジスティック回帰で正規化を行うためのパラ
メータを表します。正規化パラメータを調整することで、モデルの複雑さを制御することができます。
これはk近傍法におけるパラメータkと同じ役割であると言えます。
C
の値を小さくすると、モデルは
より複雑になります。
表5-5から
C=0.1
の時が一番良い結果だったので、そのときのバイアス- バリアン
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