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LIME & SHAP: More Holes Than Swiss Cheese? Conformal Prediction to the Rescue! 
When it comes to explaining AI models, methods like **LIME** and **SHAP** have been the go-to solutions. But let’s be honest—they have **more holes than Swiss cheese** .
**Inconsistent explanations** across runs **Lack of reliability** in real-world applications **No formal uncertainty guarantees**
**Enter Conformal Prediction.** The newly released package **[ConformaSight](https://github.com/rabia174/ConformaSight)** brings **reliable, uncertainty-aware explanations** to machine learning. Unlike LIME & SHAP, **Conformal Prediction provides rigorous, statistically valid confidence guarantees.**
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**Why ConformaSight?** 
**Trustworthy**: Ensures coverage guarantees for explanations. **Stable**: No more wildly different explanations on similar inputs. **Scalable**: Works across various ML models & domains.
If you care about **reliable AI explainability**, it's time to rethink the status quo.
Thoughts? Have you faced issues with LIME/SHAP? Let’s discuss! 