October 2017
Intermediate to advanced
480 pages
9h 49m
Japanese
Content preview from Rではじめるデータサイエンス
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333
19
章
モデル構築
19.1
はじめに
前の18章では線形モデルがどう機能するかと、モデルがデータについて何を示すかを理解する基
本ツールについて学びました。モデルの働きを学びやすいように擬似データセットに焦点を絞りまし
た。本章では、実データに焦点を当て、データの理解を助けるモデル構築をどうすれば進めていけ
るかを示します。
データをパターンと残差に分けてモデルを考えることができるという利点を活用します。可視化で
パターンを見つけ、モデルで正確に具体化します。このプロセスを繰り返し、前の応答変数をモデ
ルの残差で置き換えます。目標は、データと頭の中にある暗黙知を定量モデルの明示的な知識に移
し替えることです。そうすれば、新たな領域への適用も他の人が利用することも容易になります。
非常に巨大で複雑なデータセットでは、この作業は膨大になります。他の方法もあります。例え
ば、機械学習方式はモデルの予測能力に焦点を絞
ります。他の方式ではブラックボックスを形成す
る傾向があります。モデルは予測を生成するにはよいのですが、なぜかという理由がわかりません。
全体的には納得できる方式ですが、実世界についての知識をモデルに適用するのが難しくなります。
そのために、基本要素が変化したときに、モデルが長期的に使い続けられるのかどうかの判断が難
しくなります。ほとんどの実モデルでは、この方式とより古典的な自動化方式の組み合わせを使うだ
ろうと私は考えています。
どこでやめるかは常に問題です。どこでモデルが十分よくなるか、どこで追加投資 ...
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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.