October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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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.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.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.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.
回帰：レコメンド
141
7
章
回帰：レコメンド
高校の数学の授業で、回帰について学んだかもしれません。おそらく、そのときは回帰という名前
ではなく、最小二乗法（ordinary least squares：OLS）と呼ばれていたことでしょう。この何世紀も前
から存在する手法は、高速に実行することができ、現実の多くの問題に対して効果的に用いることが
できます。本章では、まずはじめに最小二乗法による回帰について復習し、NumPyとscikit-learnを
用いてそれを行う方法について見ていくことにします。
問題には様々なものがあり、その中には古典的手法では上手く扱えない問題が存在します。その場
合、より進んだ手法（本章の後のほうで紹介します）を用いることで、改善することができます。これ
が特に当てはまるのが、特徴量が多い場合です。それは特徴量の数がサンプルデータの数より大きい
場合（その場合、最小二乗法では正しく扱うことができません）も含みます。本章では、より進んだ手
法として、lasso、r
idge、elastic netsと呼ばれる3つの手法を紹介します。それらは最近の手法であ
り、主な開発はここ10年間の内に行われてきました。
本章の最後では、「レコメンド」について見ていきます。これは重要な分野であり、多くのアプリケー
ションに付加価値を与えることができます。また、本章ではレコメンドについての基礎を学習し、次章
ではさらに詳細にレコメンドについて学びます。
7.1
回帰を用いて物件価格を予測する
簡単な問題 ...
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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.