Interpreting machine learning models : learn model interpretability and explainability methods /

Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You'll begin by reviewing the theoretical aspects of machine...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nandi, Anirban
Otros Autores: Pal, Aditya Kumar
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Apress, [2022]
Temas:
Machine learning > Mathematical models.
Apprentissage automatique > Modèles mathématiques.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Chapter 1: The Evolution of Machine Learning
  • Chapter 2: Introduction to Model interpretability.
  • Chapter 3: Machine Learning Interpretability Taxonomy
  • Chapter 4: Common Properties of Explanations Generated by Interpretability Methods
  • Chapter 5: Human Factors in Model Interpretability
  • Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches
  • Chapter 7: Interpretable ML and Explainable ML Differences
  • Chapter 8: Framework of Model Explanations
  • Chapter 9: Feature Importance methods
  • Details and usage examples
  • Chapter 10: Detailing rule-based methods
  • Chapter 11: Detailing Counterfactual Methods
  • Chapter 12: Detailing Image interpretability methods
  • Chapter 13: Explaining text classification models
  • Chapter 14: Role of Data in Interpretability
  • Chapter 15: The 8 pitfalls of explainability methods
  • Conclusion.
  • References.

Ejemplares similares