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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118
6
章
ML
パッケージ
test = model.transform(births_test)
evaluator = ev.BinaryClassificationEvaluator(
labelCol='INFANT_ALIVE_AT_REPORT')
print(evaluator.evaluate(test,
{evaluator.metricName: "areaUnderROC"}))
print(evaluator.evaluate(test,
{evaluator.metricName: "areaUnderPR"}))
このコードの結果は次のようになります。
悪いことはまったくありません！ 実際には、精度と再現率との関係性という観点ではむしろランダム
フォレストよりもパフォーマンスが良く、ROC 以下の領域という観点でもわずかに悪化しているだけで
す。どうやら勝者は決まったようです！
6.4.3
クラスタリング
クラスタリングは、機械学習のもう1つの大きな領域です。現実の世界においては、ターゲットとなる
特徴があるという状況には恵まれないことが非常に多くあります。そのためには、データの持つパター
ンを解き明かすべく教師なし学習というパラダイムにまで立ち戻らなければなりません。
births
データセットからのクラスタ抽出
この例では、
k-means
モデルを使って birthsデータ中の相似を見つけていきます。
import pyspark.ml.clustering as clus ...
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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.