August 2015
Intermediate to advanced
312 pages
7h 28m
Japanese
Content preview from 初めてのSpark
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233
11.4 データ型
値、検定統計量、各特徴の自由度が含まれます。ラベルと特徴の値は、分類可能でなければな
りません（すなわち離散値でなければならない）。
これらのメソッド以外にも、数値データを含む
RDD
に は 、「
6.6
数値の
RDD
の操作」で示した
通り、
mean()
、
stdev()
、
sum()
といった基本的な統計機能が用意されています。加えて、
RDD
は
sample()
や
sampleByKey()
をサポートしており、データのサンプルや、階層化されたサンプルを
構築することができます。
11.4.5
分類
分類と回帰は、一般的なの形の
2
つです。これらのアルゴリズムは、ラベル付き
のトレーニングデータ（すなわち、すでに答えがわかっているサンプル）を使って、オブジェクト
の特徴から変数の予測を試みます。この
2
つの方式の違いは、予測する変数の種類です。分類で
は、変数は（すなわち、と呼ばれる有限の値の集合）です。例えば、メールが
spam
もしくは
nospam
であったり、テキストが書かれている言語がクラスです。回帰では、予想される
変数はです（例えば年齢と体重から予測される身長など）。
分類と回帰は、どちらも「
11.4
データ型」で説明した、
mllib.regression
パッケージに含ま
れる、
MLlib
の
LabeledPoint
クラスを使います。
LabeledPoint
は、
label
（この値の型は常に
Double
ですが、分類のための離散値となる整数を設定することもできます）と ...
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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.