August 2015
Intermediate to advanced
312 pages
7h 28m
Japanese
Content preview from 初めてのSpark
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238
11 章 MLlib を使った機械学習
decision-tree.html
）には、使用されているアルゴリズムの詳細な説明があります。アルゴリズム
のコストは、トレーニングのサンプル数、特徴数、
maxBins
に比例してスケールします。大規模な
データセットの場合、
maxBins
を低めに設定してモデルのトレーニングを高速に行うこともできま
すが、そうした場合には品質は低下することになります。
train()
メソッドは、
DecisionTreeModel
を返します。
DecisionTreeModel
の
predict()
を
使えば、特徴ベクトルや、ベクトルの
RDD
に対して値を予測することができます。あるいは
toDebugString()
で決定木を出力させることもできます。このオブジェクトはシリアライズ可能な
ので、
Java Serialization
を使ってセーブしておき、他のプログラムでロードすることができます。
最後に、
Spark 1.2
で は、
MLlib
に
experimental
として
RandomForest
クラスが
Java
と
Scala
で追加され
†
、決定木のアンサンブルを構築できるようになりました。この決定木のアンサン
ブルは、ランダムフォレストとも呼ばれます。これは
RandomForest.trainClassifier
および
trainRegressor
を通じて利用できます。先ほど紹介した決定木ごとのパラメータとは別に、
RandomForest
は次のパラメータを取ります。
numTrees ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.