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

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

Molnar, Christoph - Nama Orang;

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.

Ketersediaan

B20220722 005 MOL i Pradita Library (000) Tersedia - Avaliable

Informasi Detail

Judul Seri: -
No. Panggil: 005 MOL i
Penerbit: : Leanpub., 2019
Deskripsi Fisik: 314 hlm.; ilus.; 25 cm
Bahasa: English
ISBN/ISSN: 9780244768522
Klasifikasi: 000
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: Machine learning
Black Box Models
Info Detail Spesifik: -
Pernyataan Tanggungjawab: -

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