January 2020
Beginner to intermediate
728 pages
10h 26m
Japanese
Content preview from データサイエンス設計マニュアル
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268 9 章 線形回帰とロジスティック回帰
図 9 -15 ロジット関数は、スコアを確率にマッピングする
9.6.3 ロジスティック回帰
ここで 4.4.1 節で紹介したロジット関数 f(
x) を思い出そう。
f(x) =
1
1 + e
−
cx
この関数は、入力として実数 −∞ ≦ x ≦ ∞ をとり、[0,1] の範囲の値、すなわち確率を返す。図 9 -15 は、
ロジット関数 f(x) をプロットしたものである。両端ではほぼ水平だが、中央で急速に上昇する S 字形のシ
グモイド曲線になっている。
このような形を持つロジット関数は、分類境界の解釈に特に適している。特に、特定の点 p が 2 つのクラ
スの境界線 l の上下または左右にどの程度離れているかの距離を x としたときに、p に「陰性」ラベルを与
えるべきかどうかの確率を f(x) で測れるようにしたい。
ロジット関数は、1 個のパラメータだけでスコアを確率に変換する。重要な条件は、中央と両端である。
ロジット関数の結果が f(0) = 1/2 であれば、境界線上の点を取る確率は、本質的に 2 つの選択肢を選ぶコ
イン投げと同じ確率になる。境界線からの距離が広がれば広がるほど、はっきりとした判断を下せるように
なる。また、f(∞) = 1 で f (−∞) = 0 となる。
距離の関数としての確信度は、スケーリング定数の c で調節できる。c を 0 に近い値にすると、正から負
への遷移は非常に緩やかになる。それに対し、c を十分大きい値にすると、ロジット ...
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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.