What are the limitations of LIME and SHAP in interpreting machine learning models?
LIME and SHAP are powerful tools, but they have limitations. For instance, LIME can be sensitive to the choice of the local model, and SHAP can be computationally expensive for large datasets.
Can you explain why LIME's sensitivity to local models is a problem?
Sure! LIME approximates the model locally, so if the local model is poorly chosen, it can lead to misleading interpretations of feature importance.
What about SHAP? Why is it computationally expensive?
SHAP values require calculating the contribution of each feature across all possible combinations, which can be very resource-intensive, especially with many features.
Are there any alternatives to LIME and SHAP?
Yes, alternatives include methods like Integrated Gradients, DeepLIFT, and Anchors, each with its own strengths and weaknesses.
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