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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5
章
MLlib
77
5
章
MLlib
前章では、モデリングに備えてデータを整える方法を学びました。本章では、学んだ知識を実際に活
用し、PySparkのMLlib パッケージを使ってクラシフィケーションモデルを構築します。
MLlibは、Machine Learning Libraryを意味しています。MLlib は現在メンテナンスモードになっ
ており、アクティブな開発は行われていない（そしておそらくゆくゆくは非推奨となる）状況にありま
すが、少なくとも多少の機能をここで取り上げるだけの価値はあります。加えて、現時点でストリーミ
ングでのモデルのトレーニングをサポートしているのは MLlibのみです。
Spark 2.0
からは、
RDD
を利用する
MLlib
ではなく、
DataFrame
を扱う
ML
がメインの機械
学習ライブラリになりました。
MLlib
のドキュメンテーションは
http://spark.apache.org/
docs/latest/api/python/pyspark.mllib.html
にあります。
本章で学ぶことは次のとおりです。
●
MLlib でモデリングのためのデータの準備
●
統計的検定の実行
●
ロジスティック回帰による幼児の生存確率の予測
●
最も予測に適した特徴の選択とランダムフォレストモデルのトレーニング
5.1
MLlib
パッケージの概要
高レベルで見れば、MLlibには機械学習に関する3つの機能があります。
●
データの準備 特徴抽出、変換、選択、カテゴリ型の特徴のハッ
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.