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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6.2
ML
による乳幼児の生存確率の予測
103
この構造を
Pipeline
にするのはごく簡単です。
pipeline = Pipeline(stages=[
encoder,
featuresCreator,
logistic
])
これだけです！ ついに
Pipeline
ができたのでモデルで推定が行えるようになります。まったく
もってその通りですね！
6.2.5
モデルのフィッティング
モデルをフィットさせる前に、データセットをトレーニング用とテスト用のデータセットに分割しな
ければなりません。DataFrame APIには便利な
.randomSplit(...)
メソッドが用意されています。
births_train, births_test = births \
.randomSplit([0.7, 0.3], seed=666)
1つめのパラメータは、それぞれ
births_train
と
births_test
となるデータセットの比率のリ
ストです。
seed
パラメータは、乱数生成器のシードです。
リストの要素の合計が
1
になるかぎりデータセットはいくつにでも分割でき、指定した数の部
分集合が得られます。たとえば次のようにすれば、誕生記録のデータセットは
3
つに分割され
ます。
train, test, val = births.\
randomSplit([0.7, 0.2, 0.1], seed=666)
このコードは誕生記録のデータセットから
70%
を抜き出して
train
オブジェクトに、
20%
を
test ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.