What are the different explainable AI techniques and how do they compare?

What are the different explainable AI techniques and how do they compare?

There are several explainable AI techniques, including LIME, SHAP, and decision trees. LIME provides local explanations, while SHAP offers a unified measure of feature importance. Decision trees are inherently interpretable. Would you like a deeper comparison?

Yes, can you explain how LIME and SHAP differ in their approach?

LIME focuses on approximating the model locally by perturbing the input data and observing changes in predictions. SHAP, on the other hand, uses game theory to assign each feature an importance value based on its contribution to the prediction. This makes SHAP more consistent across different models.

Which technique is generally preferred in practice?

It often depends on the use case. SHAP is preferred for its consistency and theoretical foundation, while LIME is favored for its simplicity and speed in generating explanations.

Are there any limitations to these techniques?

Yes, both techniques have limitations. LIME can be sensitive to the choice of perturbations, and SHAP can be computationally expensive for large datasets. It's important to choose the right technique based on your specific needs.