November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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158
8
章
TensorFrames
特徴選択と特徴抽出との比較に関する参考資料としては、
"What is dimensionality reduction?
What is the difference between feature selection and extraction?"
（
http://datascience.
stackexchange.com/questions/130/what-is-dimensionality-reduction-what-is-the-
difference-between-feature-selecti/132#132
）をお勧めします。
8.1.3
データとアルゴリズムの架け橋
レストランのレコメンデーションを例として、特徴選択という観点から特徴と特徴エンジニアリング
の間に橋を架けていきましょう。
潜在的な特徴
どのように特徴を選択するべきか？
場所
料理の種類
場所の規模
テイクアウト？
子 ど も 向 き？
ビジネス客向き？
予約可能？
BI
ツールやスプレッドシートで分析するか？
機械学習のアルゴリズムを使うか？
回帰
アルゴリズム
クラ シ フィケ ー ション
ここで扱うのは単純化されたモデルではありますが、この例えは実践的な機械学習の基本的な前提を
示すものです。データを分析し、このレストランのレコメンデーションモデルの主要な特徴を決定する
のは、データサイエンティストの役割となるでしょう。
このレストランのレコメンデーションの場合、場所と料理の種類が主要な要 ...
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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.