The relative performance in err/watts/time compared to deep learning for feature selection instead of principal component analysis and standard xgboost or tabular xt TODO for optimization given the indicating features.

XAI: Explainable AI: https://en.wikipedia.org/wiki/Explainable_artificial_intelli...

/? XAI , #XAI , Explain, EXPLAIN PLAN , error/energy/time

From "Interpretable graph neural networks for tabular data" (2023) https://news.ycombinator.com/item?id=37269881 :

> TabPFN: https://github.com/automl/TabPFN .. https://x.com/FrankRHutter/status/1583410845307977733 [2022]

"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second" (2022) https://arxiv.org/abs/2308.08945

> FWIU TabPFN is Bayesian-calibrated/trained with better performance than xgboost for non-categorical data

From https://news.ycombinator.com/item?id=34619013 :

> /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22

- (Many other great resources)

- https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... :

> Post model-creation analysis, ML interpretation/explainability

> /? awesome "explainable ai" "XAI"