November 2020
Intermediate to advanced
396 pages
8h 34m
Japanese
book
データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
by Peter Bruce, Andrew Bruce, Peter Gedeck, 黒川 利明, 大橋 真也
Content preview from データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
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338
■
7
章 教師なし学習
対応する
Python
コードは次の通り。
（
Python
）
loadings = pd.DataFrame(sp_pca1.components_[0:2, :],
columns=top_sp1.columns)
loadings.transpose()
最初の
2
つの主成分では、ほぼ完全に
GOOGL
と
AMZN
が優勢である。
GOOGL
と
AMZN
の株価変動が変動性の大勢を占めているからだ。
この状況を処理するには、変数をスケールし直す（「7.5.1 変数のスケーリング」参
照）か、分析から優勢な変数を取り除いて、それらを別途処理するかだ。「正しい」とさ
れている方法はなく、処理はアプリケーションに依存する。
図 7-13 GOOGLとAMZNを含めたS&P500 のトップ株式のPCAによるスクリープロット
7.5.3
カテゴリデータと
Gower
距離
カテゴリデータの場合、（順序ファクタなら）順位付けによるか、一連の二値（ダミー）
変数でエンコーディングするかして、数量データに変換する必要がある。データが連
続変数と二値変数の混合なら、通常は、変数をスケールして同じ範囲になるようにする
（「7.5.1 変数のスケーリング」参照）。よく使われるのが、Gower 距離だ。
7.5
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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.