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
105
print(evaluator.evaluate(test_model,
{evaluator.metricName: 'areaUnderROC'}))
print(evaluator.evaluate(test_model,
{evaluator.metricName: 'areaUnderPR'}))
このコードの結果は次のようになります。
ROC 以下の領域は 74%で、PR 以下の領域は 71% になっており、モデルはうまく定義されているもの
の、傑出しているというほどではないことがわかります。他の特徴も使えばパフォーマンスを押し上げ
ることもできるかもしれませんが、それは本章で目標とするところではありません（さらに言うなら本
書 の 目 標 で も あ り ま せ ん ）。
6.2.7
モデルのセーブ
PySpark では、
Pipeline
の定義をセーブしておき、後から再利用できます。パイプラインの構造だ
けではなく、
Transformers
や
Estimators
の定義もセーブされます。
pipelinePath = './infant_oneHotEncoder_Logistic_Pipeline'
pipeline.write().overwrite().save(pipelinePath)
したがって、セーブされた内容を後からロードし、そのまま
.fit(...)
して予測できます。
loadedPipeline = Pipeline.load(pipelinePath) ...
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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.