Summary  
This chapter covers the concept of model-specific versus model-agnostic explainability methods, contrasting techniques that leverage a model’s internal structure with black-box approaches that analyze only inputs and outputs.  

General domain of usage  
Machine learning model interpretability

Understanding the difference between **model-specific** and **model-agnostic** explainability methods is essential for choosing the right approach to interpret machine learning models. **Model-specific methods** are designed for particular types of models and take advantage of their internal structure. For example, decision trees can be easily visualized and interpreted because their decisions follow a clear, rule-based path from root to leaf. You can directly trace how features influence predictions by following the splits in the tree. On the other hand, **model-agnostic methods** are designed to work with any machine learning model, regardless of its internal mechanics. These techniques treat the model as a black box—they analyze the input-output relationship without requiring access to the model’s internal parameters or structure.

**Popular model-agnostic techniques:**
- LIME (Local Interpretable Model-agnostic Explanations);
- SHAP (SHapley Additive exPlanations);
- Permutation Feature Importance.

When deciding between model-specific and model-agnostic methods, consider their unique strengths and weaknesses. The following table summarizes key differences:



Which of the following best defines a model-agnostic explainability method?

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Model-Specific vs. Model-Agnostic Methods