What is model interpretability in machine learning?

What is model interpretability in machine learning?

Model interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model.

Why is it important to have interpretable models?

Interpretable models help build trust, ensure compliance with regulations, and allow for better debugging and improvement of the model.

Can you give examples of interpretable models?

Sure! Examples include linear regression, decision trees, and rule-based models, which are easier to understand than complex models.

What techniques are used to interpret complex models?

Techniques like LIME, SHAP, and feature importance scores are commonly used to interpret complex models like neural networks.

How does interpretability affect model performance?

There's often a trade-off; simpler, interpretable models may perform less well than complex models, but they provide better insights.