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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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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
November 2020
Intermediate to advanced
396 pages
8h 34m
Japanese
O'Reilly Japan, Inc.
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
336
■
7
章 教師なし学習
centers0 <- scale(centers0, center=-attr(df0, 'scaled:center'),
scale=FALSE)
centers0 <- data.frame(size=km0$size, centers0)
round(centers0, digits=2)
size loan_amnt annual_inc revol_bal open_acc dti revol_util
1 7355 10467.65 51134.87 11523.31 7.48 15.78 77.73
2 5309 10363.43 53523.09 6038.26 8.68 11.32 30.70
3 3713 25894.07 116185.91 32797.67 12.41 16.22 66.14
4 6294 13361.61 55596.65 16375.27 14.25 24.23 59.61
Python
では
scikit-learn
の
StandardScaler
が使える。
inverse_transform
メ
ソッドでクラスタ中心を元のスケールに変換できる。
(
Python
)
scaler = preprocessing.StandardScaler()
df0 = scaler.fit_transform(df * 1.0)
kmeans = KMeans(n_clusters=4, random_state=1).fit(df0)
counts ...
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Publisher Resources

ISBN: 9784873119267Other