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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
[2022].
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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.