Open xnuohz opened 6 months ago
Thanks. As you mentioned, mutual information sorting
and ExcelFormer
improve performance through transformation capabilities. However, I want to discuss how much different features contribute to the final prediction result. For example, user behavioral features are important in recommender systems, so their feature importance should be high. pytorch-frame is good to use. It allows me to quickly obtain benchmark results on real-world datasets to determine whether NNs or GBDTs are better. I'm unsure if the functionality to evaluate feature importance is worth integrating as a module into pytorch-frame.
I think you can use Captum https://captum.ai/ to have a try cc @weihua916 we can also integrate this in PyT?
Yes, Captum implemented many interpretability methods, Feature Permutation and SHAP are part of them.
Is there any update or roadmap related to it? 👀
Feature
Support feature importance in tabular data scenarios.
Ideas