September 2025
Intermediate to advanced
476 pages
7h 51m
Japanese
book
高リスク分野のための機械学習 ―責任あるAI構築のための実践アプローチ
by Patrick Hall, James Curtis, Parul Pandey, 高江洲 勲, 伊東 道明, 園田 道夫, 北條 孝佳, 石川 太一
Content preview from 高リスク分野のための機械学習 ―責任あるAI構築のための実践アプローチ
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6章説明可能なブースティング・マシンおよびXGBoostの説明
本章では、解釈可能なモデルと事後説明について、消費者金融に関連するインタラクティブな例（https://github.com/ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book/tree/main/code/Chapter-6）を用いて探求する。また、2章で議論されたアプローチを、解釈可能なブースティング・マシン（EBM）、単調性制約付きXGBoostモデル、および事後説明技術を用いて適用する。まず、加法性、制約、部分的依存、個別条件期待（ICE）、Shapley加法説明（SHAP）、およびモデル文書化の概念を復習することから始める。
次に、ペナルティ付き回帰から一般化加法モデル（GAM）、さらにEBMへと構築を進めながら、クレジット与信問題の例を探求する。より単純なモデルからより複雑なモデルへと進む過程で、非線形性と相互作用を導入する際の明確かつ意図的なトレードオフを記録しつつ、加法モデルを使用してほぼ完全な解釈可能性を維持した例のデフォルト確率分類器を構築する。
![[注記]](/api/v2/epubs/urn:orm:book:9784814401277/files/images/note.png) |
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2章を思い出してほしい。「解釈」とは、高次元で意味のある精神的表現であり、刺激を文脈化し、人間の背景知識を活用するものである。一方、「説明」は、複雑なプロセスを記述しようとする低次元で詳細な精神的表現である。解釈は説明よりもはるかに高い基準を持ち、技術的アプローチだけではほとんど達成されない。 ... |
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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.