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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180
9
章 クラス分類
III
：音楽ジャンル分類
9.4.3
混同行列を用いて多クラス問題の正解率を計測する
多クラス問題を扱う場合、どれだけ正しく分類できたかということだけを興味の対象とすべきではあ
りません。正しく分類できたかということに加えて、どのジャンルにおいて誤分類が多いかを見ていく
べきでしょう。これを行うためには、いわゆる混合行列（
confusion matrix
）というものを用いま
す。
>>> from sklearn.metrics import confusion_matrix
>>> cm = confusion_matrix(y_test, y_pred)
>>> print(cm)
[[26 1 2 0 0 2]
[ 4 7 5 0 5 3]
[ 1 2 14 2 8 3]
[ 5 4 7 3 7 5]
[0 0 10 2 10 12]
[1 0 4 0 13 12]]
上の結果は、テストデータに対して分類器が予測した結果であり、ラベルごとの分布を表していま
す。私たちはは6 つのジャンルを扱うため、6×6の行列が得られます。行列の最初の行が意味する内
容は、31曲（26＋1＋2＋2＝31）のクラシックがあり、その内
26
曲をクラシックと分類し、1曲を
ジャズ、2曲をカントリー、2曲をメタルと予測した、という結果を表します。行列の対角成分が正し
い分類結果を示します。最初の行では31曲のうち26曲を正しく分類することができ、5曲を誤った分
類をしたことがわかります。これはそれほど
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