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Interpretable machine learning with Python: learn to build interpretable high-performance models with hands-on real-world examples.
- Publication Year :
- 2020
-
Abstract
- Summary: The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.
- Subjects :
- Machine Learning
Python
Programming
AI
Subjects
Details
- Language :
- English
- ISBN :
- 9781800203907 (hbk.)
- ISBNs :
- 9781800203907
- Database :
- Jio Institute Digital Library Catalog
- Journal :
- Interpretable machine learning with Python: learn to build interpretable high-performance models with hands-on real-world examples / Serg Masis
- Notes :
- Includes bibliographical references and index.
- Publication Type :
- Book
- Accession number :
- jlc.oai.folio.org.fs00001072.e9b786fe.3eee.4c50.8d1b.5d07c466be8f
- Document Type :
- Non-fiction