How do I choose between Lime and SHAP for model interpretation?

I'm trying to decide between using Lime and SHAP for interpreting my machine learning model. Any advice?

Both Lime and SHAP are great tools for model interpretation, but they have different strengths. Lime is more local, focusing on individual predictions, while SHAP provides a global view of feature importance.

What do you mean by local and global interpretations?

Local interpretation means understanding how a model makes predictions for a specific instance, which is what Lime does. Global interpretation, like SHAP, helps you see the overall impact of features across all predictions.

Are there any specific scenarios where one is better than the other?

Yes! If you need to explain a single prediction, Lime might be more intuitive. However, if you want to understand feature importance across your entire dataset, SHAP is usually the better choice.

Can I use both methods together?

Absolutely! Using both can provide a comprehensive understanding of your model's behavior, combining local insights from Lime with global insights from SHAP.