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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4.4
カーネル
PCA
による異常検出
103
anomalyScoresSparsePCA = anomalyScores(X_train, X_train_sparsePCA_inverse)
preds = plotResults(y_train, anomalyScoresSparsePCA, True)
適合率
-
再現率曲線と
ROC
曲線をプロットしてみよう。
図4-6 スパースPCA による27 主成分を用いた場合の結果
図4-6からわかるように、結果は通常の
PCA
とほとんど同一だ。スパース
PCA
はスパースな表現を
与えるだけで、通常の
PCA
ととてもよく似ているので、この結果は予測された通りだ。
GitHub
上 の コ ー ド（
https://github.com/aapatel09/handson-unsupervised-learning
）を 使 っ て 、 主
成分の数や
alpha
パラメータを変更して実験してみよう。しかし、われわれが試した範囲ではこれが
最良のスパース
PCA
ベース不正検出システムだ。
4.4
カーネル
PCA
による異常検出
次にカーネル
PCA
を用いた不正検出システムを試してみよう。カーネル
PCA
は非線形の
PCA
なの
で、不正なトランザクションと真正なトランザクションを線形に分離できないのであれば、この方法を
使う意味があるはずだ。
ここで、生成する成分の数、カーネルの種類（前章と同様に
RBF
カーネルを用いる）、
gamma
パラ
メ ー タ（ デ フ ォ ル ト で は
1/
特徴量の
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.