Interpretable Machine Learning: A Guide For Making Black Box Models Explainable (Record no. 98796)

MARC details
000 -LEADER
fixed length control field 02198 a2200181 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241112b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355428370
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter MOL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Molnar Christoph
245 ## - TITLE STATEMENT
Title Interpretable Machine Learning: A Guide For Making Black Box Models Explainable
250 ## - EDITION STATEMENT
Edition statement 1st Ed
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff Publishers & Distributors Pvt. Ltd.
Date of publication, distribution, etc 2024
Place of publication, distribution, etc India
300 ## - PHYSICAL DESCRIPTION
Extent 332
520 ## - Remark
Summary, etc Shroff Publishers do not endorse the preview pages of kindle linked to our ISBNs. All Indian Reprints of Christoph Molnar are Printed in Grayscale.<br/><br/>Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.<br/><br/>After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local ects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks.<br/><br/>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. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.<br/><br/>Source: https://www.amazon.in/Interpretable-Machine-Learning-Explainable-Grayscale/dp/9355428375/ref=sr_1_1?crid=1N14NLJCGA4A7&dib=eyJ2IjoiMSJ9.BHDpIlE4XWCWEzyqoewAPA.iK-rqUgMqo_PGmLcBGqKcxN--YUnlD2CeOJTnhY-hIg&dib_tag=se&keywords=9789355428370&qid=1731420676&sprefix=%2Caps%2C447&sr=8-1
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a Business Analytics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Collection Type Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 28/10/2024 Shroff Publishers 1160.00 Foreign Book 005.74015 MOL 119564 28/10/2024 1450.00 Book

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