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

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