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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36
2
章 機械学習プロジェクトのはじめから終わりまで
V23 275663
V24 275663
V25 275663
V26 275663
V27 275663
V28 275663
Amount 32767
Class 2
dtype: int64
2.3.3
特徴量行列とラベル配列の作成
特徴量行列
X
を作成して標準化し、（
1
は不正、
0
は真正を表す）ラベル配列
y
を分けよう。あとで、
これら
2
つを機械学習アルゴリズムに投入して学習させることになる。
まず、特徴量行列
X
と配列
y
を作成しよう。
dataX = data.copy().drop(['Class'],axis=1)
dataY = data['Class'].copy()
次に、特徴量行列
X
の標準化を行う。つまり、特徴量行列を、タイムスタンプを除くそれぞれの特
徴量が平均
0
、標準偏差
1
になるようにスケール変換する。
featuresToScale = dataX.drop(['Time'],axis=1).columns
sX = pp.StandardScaler(copy=True)
dataX.loc[:,featuresToScale] = sX.fit_transform(dataX[featuresToScale])
表2-3に示す通り、標準化された特徴量は平均
0
、標準偏差
1
になっている。
表2-3 スケール変換された特徴量の要約統計量
Time V1 V2 V3 V4
count 284807.000000 2.848070e+05 2.848070 ...
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