Model interpretability refers to the ability to understand and explain how a machine learning model makes predictions. It involves making the decision-making process of complex models transparent and understandable.
- Trust and Adoption: Interpretable models are more likely to be trusted and adopted by users, stakeholders, and regulatory bodies.
- Ethical Considerations: Understanding model decisions is crucial for addressing ethical concerns, especially in sensitive applications like healthcare or finance.
I. Techniques for Model Interpretability:
1. Feature Importance:
a. Permutation Importance:
- This method involves randomly permuting the values of a single feature and measuring the impact on the model’s performance. The decrease in performance indicates the importance of the feature.
b. SHAP Values (SHapley Additive exPlanations):
- SHAP values allocate the contribution of each feature to a prediction, considering all possible combinations. They provide a unified measure of feature importance and help explain individual predictions.
