Interpretable machine learning with Python : build explainable, fair, and robust high-performance models with hands-on, real-world examples /

Interpretable machine learning with Python : build explainable, fair, and robust high-performance models with hands-on, real-world examples /

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Bibliographic Details
Author / Creator:	Masís, Serg, author.
Edition:	Second edition.
Imprint:	Birmingham : Mumbai : Packt Publishing, 2023. ©2023.
Description:	xxvii, 576 pages : illustrations ; 24 cm
Language:	English
Series:	Expert insight Expert insight.
Subject:	Machine learning. Python (Computer program language) Data mining. Machine learning Python (Computer program language)
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13560361

Hidden Bibliographic Details
Other authors / contributors:	Molak, Aleksander, writer of foreword. Rothman, Denis, writer of foreword.
ISBN:	9781803235424 180323542X 9781803243627 (ebook)
Notes:	"Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data."-- Page [4] of cover. Includes bibliographical references and index.

Eckhart

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Call Number:	QA76.73.P98 M37 2023
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