Interpretable machine learning with Python

• Verfasst von: Masís, Serg [VerfasserIn]
• Titel: Interpretable machine learning with Python
• Titelzusatz: build explainable, fair, and robust high-performance models with hands-on, real-world examples
• Mitwirkende: Molak, Aleksander [MitwirkendeR]
• Mitwirkende: Rothman, Denis [MitwirkendeR]
• Verf.angabe: Serg Masís ; forewords by Aleksander Molak, Denis Rothman
• Ausgabe: Second edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2023
• Umfang: 1 online resource (606 pages)
• Illustrationen: illustrations
• Gesamttitel/Reihe: Expert insight
• Fussnoten: Includes bibliographical references and index
• ISBN: 978-1-80323-542-4
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1871287316
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4418438823
• Lokale URL Inst.: Zum Volltext

Abstract

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. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.