Model-agnostic interpretability methods are techniques that can be applied to any machine learning model, regardless of its underlying architecture or complexity. These methods aim to provide insights into the behavior of models, make predictions more understandable, and foster trust in the decision-making process. This guide explores various model-agnostic interpretability methods, their key concepts, and practical applications.
I. Key Concepts:
1. Model-Agnostic Nature:
- Model-agnostic methods do not depend on the internal structure or specific characteristics of the underlying model being interpreted.
- They are designed to be versatile and applicable to a wide range of machine learning models, including black-box models.
2. Interpretable Approximations:
- Model-agnostic methods often create interpretable approximations or surrogate models that capture the behavior of the original model in a more transparent form.
3. Local vs. Global Interpretability:
- Model-agnostic methods can provide both local and global interpretability. Local interpretability focuses on…
