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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90
5
章
MLlib
5.5.2
最も予測可能な特徴のみを選択する
少ない特徴で正確に分類予測ができるモデルは、より複雑なモデルよりも優れていると言えます。
MLlib では、カイ二乗セレクタを使って最も予想に役立つ特徴を選び出すことができます。
次にその方法を示します。
selector = ft.ChiSqSelector(4).fit(births_train)
topFeatures_train = (
births_train.map(lambda row: row.label) \
.zip(selector \
.transform(births_train \
.map(lambda row: row.features)))
).map(lambda row: reg.LabeledPoint(row[0], row[1]))
topFeatures_test = (
births_test.map(lambda row: row.label) \
.zip(selector \
.transform(births_test \
.map(lambda row: row.features)))
).map(lambda row: reg.LabeledPoint(row[0], row[1]))
ここでは最も予測に役立つ 4つの特徴をセレクタに返させ、
births_train
データセットを使って
セレクタをトレーニングしています。そしてそのモデルを使い、トレーニングおよびテス ...
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