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Interpretable machine learning: a guide for making black box models interpretable

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

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Доступность

B20220722

005 MOL i

Pradita Library (000)

Доступно - Avaliable

Detail Information

Название серии

-

Номер вызова

005 MOL i

Издатель

: Leanpub., 2019

Сопоставление

314 hlm.; ilus.; 25 cm

Язык

English

ISBN/ISSN

9780244768522

Классификация

000

Content Type

-

Media Type

-

Carrier Type

-

Редакция

-

Тема(ы)

Machine learning
Black Box Models

Специфические детали информации

-

Заявление об ответственности

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Other version/related

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