explainable machine learning models (73 resultados)

XVII, 299 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. The Springer Series on Challenges in Machine Learning. Sprache: Englisch.

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hardcover. Condición: Gut. Seiten; 9781394185849.3 Gewicht in Gramm: 1.

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Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. 'Models & Methodologies for Explainable Machine Learning' presents a detailed examination of techniques that enhance the interpretability of machine learning models. It discusses various frameworks, algorithms, and evaluation metrics that facilitate understanding complex AI systems. This book is essential for practitioners aiming to develop transparent and accountable AI solutions. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Models & Methodologies for Explainable Machine Learning presents a comprehensive guide to the principles and best practices of explainable machine learning, providing a clear and concise roadmap for navigating the complexities of this rapidly evolving field. By exploring the intricacies of explainable machine learning, this book equips readers with the knowledge and skills necessary to design, develop, and implement machine learning models that are accurate, efficient, and transparent. From the fundamentals of machine learning and model interpretability to the advanced techniques of feature attribution and model explainability, this guide offers a practical approach to creating machine learning models that can collect and analyze data, make informed decisions, and optimize business outcomes. With Models & Methodologies for Explainable Machine Learning, readers will gain a deeper understanding of the complex relationships between machine learning, explainability, and data-driven decision making, enabling them to create innovative solutions that transform industries and revolutionize the way we approach business growth. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

paperback. Condición: New. New. book.

Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Explainable machine learning is a rapidly evolving field with far-reaching implications for artificial intelligence, machine learning, and data science. This book provides a comprehensive guide to the models and methodologies of explainable machine learning, offering insights into the latest trends, technologies, and best practices. By examining the applications and benefits of explainable machine learning, from model interpretability to feature attribution, Models & Methodologies for Explainable Machine Learning equips readers with the knowledge and skills necessary to drive innovation and growth in their organizations. With its focus on real-world examples and practical advice, this book is an essential resource for anyone looking to stay ahead of the curve in explainable machine learning. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

paperback. Condición: New. Paperback. Pub Date: 2021-03-01 Pages: 244 Language: Chinese Publisher: Electronic Industry Press. Although machine learning has great potential for improving product performance and advancing research. it cannot explain their predictions. This is A major obstacle currently facing.?Explainable Machine Learning: A Guide to Understanding the Interpretability of Black Box Models is a book about making machine learning models and their decisions interpretable.?This book explores the concept of in.