[FEATURE] Enhance LLM-Powered Explanations for Edge Cases and Customization

[nazeyanehal]

Description

Currently, ExplainableAI's LLM-powered explanations provide general insights into model predictions, which are helpful in most cases. However, there is a need for more tailored explanations when the model encounters edge cases, such as poor performance or imbalanced datasets. Additionally, there is no option for users to customize the level of detail in the explanations, which limits the tool's usability for both technical and non-technical users.

Problem it Solves

This feature will address the problem of generic LLM explanations by offering more specific insights when models perform poorly or encounter problematic data (e.g., outliers, imbalanced classes). It will also solve the issue of inflexible explanations by allowing users to control the depth of explanation, catering to both novice users and more advanced technical users who need detailed analyses.

Proposed Solution

Enhance LLM explanations to identify and explain edge cases, such as:
Poor model performance (e.g., low accuracy, high error rates).
Imbalanced datasets leading to biased predictions.
Outliers or anomalies in the data.
Add an optional parameter (e.g., explanation_level) that allows users to select between a high-level summary for non-technical users or an in-depth analysis for those who require detailed insights, such as feature importance breakdowns and model-specific diagnostics.

Alternatives Considered

One alternative would be to manually explain these edge cases by interpreting the results post-hoc, but this process can be time-consuming and inefficient, especially for non-experts. Additionally, separate documentation or tutorials could be provided to explain these scenarios, but having it built into the tool as an automated feature would be much more user-friendly and efficient.

[github-actions]

👋 Thank you for raising an issue! We appreciate your effort in helping us improve. Our team will review it shortly. Stay tuned!

[ombhojane]

interesting! @nazeyanehal go ahead!

you may try out:
enhance the prompting,
feed the additional data, say EDA data we've performed, SHAP Analysis, etc.

[nazeyanehal]

@ombhojane I've completed the enhancement with EDA and SHAP integration for the LLM explanations. Let me know if you need any changes!

Thanks!

[ombhojane]

hey @nazeyanehal can you mension the link of PR