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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17.5
ひとつにまとめる
■
241
最後に、経験値が低（
Junior
）の候補者に対しても同じことを行います。この結果
により
PhD
の有無を使った分割を行います。経験値が低で
PhD
を持っていない候補者
は常に
True
であり、
PhD
を持っている場合は常に
False
です。
完成した決定木を図 17-3 に示します。
Senior Junior
Mid
No
Yes
No
Yes
経験値？
ツイート
?
採用
! PhD ?
不採用採用
!
不採用採用
!
図 17-3 最終的な決定木
17.5
ひとつにまとめる
このアルゴリズムがどのように働くのかを見てきました。このアルゴリズムを、より
汎用的に実装しましょう。そのためには、まず木構造をどのように表現するのかを決
めなければなりません。できる限り簡単な表現を使います。木の構成要素は次のうち
のいずれかです。
Leaf
（末端ノード）
ある値の予測
Split
（分岐）
分岐のための属性、ある属性に対する部分木、場合によっては不明な値が見
つかった場合に使用するデフォルト値
from typing import NamedTuple, Union, Any
class Leaf(NamedTuple):
value: Any
242
■
17
章 決定木
class Split(NamedTuple): ...
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