May 2020
Intermediate to advanced
456 pages
9h 13m
Japanese
Content preview from ゼロからはじめるデータサイエンス 第2版 ―Pythonで学ぶ基本と実践
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■
17
章 決定木
この手法の問題点は、異なる多くの値を持つ属性で分割を行った場合に、過学習
によってエントロピーが低い値となる点です。例えば、過去の取引履歴を学習用
データとして、どの顧客が住宅ローンの債務不履行となる可能性が高いかを見極
める銀行向け決定木を作っているとしましょう。データセットには、各顧客の社
会保障番号（
SSN
：
Social Security Number
）が入っているとします。
SSN
を
使って分割を行うと、
1
人ずつの部分集合を作り出し、それぞれのエントロピー
は必然的に
0
となります。
SSN
に頼ったモデルは、学習用データから外れた場合
の汎用性を明らかに持ちません。この理由から、決定木を作る際には多くの値を
持つ属性を避ける（または可能なら値をまとめる）ような作り方を考えなければ
なりません。
17.4
決定木の生成
面接した候補者のデータが人材担当部長より提供されました。名前付きタプルの
形式で各候補者ごとに（指定した通りの仕様で）経験値、得意なプログラミング言語、
Twitter
で積極的にツイートしているか否か、
PhD
を持っているか否か、面接の成否が
構成されています。
from typing import NamedTuple, Optional
class Candidate(NamedTuple):
level: str
lang: str
tweets: bool
phd: bool
did_well: Optional[bool] = None #
値なしを許容する ...
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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.