What are some effective methods for AI interpretability?
There are several methods for AI interpretability, including LIME, SHAP, and feature importance analysis. Would you like to know more about any specific method?
Can you explain LIME?
LIME, or Local Interpretable Model-agnostic Explanations, works by approximating the model locally with an interpretable model. It helps to understand individual predictions.
What about SHAP?
SHAP, or SHapley Additive exPlanations, assigns each feature an importance value for a particular prediction, based on cooperative game theory. It's great for understanding the contribution of each feature.
Are there any visual tools for interpretability?
Yes, tools like LIME and SHAP also provide visualizations to help interpret the results, making it easier to understand model behavior.
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