Applied machine learning explainability techniques : make ML models explainable and trustworthy for practical applications Using LIME, SHAP, and more

Applied machine learning explainability techniques make ML models explainable and trustworthy for practical applications Using LIME, SHAP, and more

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features Explore various explainability methods for designing robust and scalable explainable ML systems Use XAI frameworks such as LI...

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Detalles Bibliográficos
Otros Autores: Bhattacharya, Aditya, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing [2022]
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009672533306719
Tabla de Contenidos:
  • Table of Contents Foundational Concepts of Explainability Techniques Model Explainability Methods Data-Centric Approaches LIME for Model Interpretability Practical Exposure to Using LIME in ML Model Interpretability Using SHAP Practical Exposure to Using SHAP in ML Human-Friendly Explanations with TCAV Other Popular XAI Frameworks XAI Industry Best Practices End User-Centered Artificial Intelligence.

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