November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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70
4
章 データのモデリングの準備
とんど同様に重要なことです。強い相関関係を持つ（すなわち共線性を持 つ）列群が 含まれていると、
モデルが予測の付かない挙動を示したり、必要以上に複雑なものになったりします。
多重共線性について、筆者は
"Practical Data Analysis Cookbook, Packt Publishing"
（
https://www.packtpub.com/big-data-and-business-intelligence/practical-data-analysis-
cookbook
）という書籍の
5
章、Introducing MLlibのIdentifying and tackling multicollinearity
というタイトルのセクションで詳しく書いています。
データを DataFrameにできていれば、PySparkで相関を計算するのはとても簡単です。難点なの
は、
.corr(...)
メソッドが現時点でサポートしているのがピアソン相関だけだということと、次に示
すとおり計算できるのがペアワイズ相関だけだということです。
fraud_df.corr('balance', 'numTrans')
相関行列は、次のスクリプトで生成できます。
n_numerical = len(numerical)
corr = []
for i in range(0, n_numerical):
temp = [None] * i
for j in range(i, ...
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