July 2014
Beginner to intermediate
456 pages
11h 26m
Japanese
Content preview from 戦略的データサイエンス入門 ―ビジネスに活かすコンセプトとテクニック
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96
4 章 モデルをデータにフィットさせる
出すことは不可能です。そこでデータサイエンティストは何らかの信念
†
と経験に基づいて
目的変数を選びます。そのためのとても効果があるとされる方法が、これまでにいくつか発
明されてきました。そのうちの
1
つはサポートベクターマシン（
support vector machine
：
SVM
）と呼ばれるものです。サポートベクターマシンについては、簡単な目的関数の例を示
した後で少し詳しく解説します。さらにその後で、分類ではなく回帰についての線形モデル
を説明します。最後に、最も役に立つデータマイニング技法の
1
つであるロジスティック回
帰（
logistic regression
）を解説して締めくくります。本当は、ロジスティック回帰という名
前は不適切な名前です。ロジスティック回帰は本当の回帰、つまり数値の推定をするもので
はないからです。ロジスティック回帰は線形モデルを応用してクラス確率推定を行うための
ものであり、多くの問題においてとても役に立ちます。
線形回帰、ロジスティック回帰、サポートベクターマシンは、どれも（線形）モデルを
データにフィットさせる技法であり、とてもよく似ています。それらの違いのうち重要な点
は異なる目的関数を使っているということです。
4.1.3
データから線形判別器を見つけ出す例
線形判別関数を説明するため、
UCI
のデータセットリポジトリ（
http://archive.ics.
uci.edu/ml/
）にある
Iris
（アイリス）
‡
のデータセット（
http://archive.ics.uci.edu/ml/ ...
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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.