Interpretable graph neural networks for tabular data

[PaulHoule]

https://arxiv.org/abs/2308.08945

[westurner]

> At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead

TabPFN: https://github.com/automl/TabPFN https://twitter.com/FrankRHutter/status/1583410845307977733

"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

[PaulHoule → westurner]

Right, the significance of the original article and the related field of research is that ChatGPT-like models don't handle tabular data well and there's a lot of need for things that do.

[westurner → PaulHoule]

There are multiple metrics to optimize for when optimizing.

FWIU, from the diagram in the photo in the linked tweet, which is similar to a diagram on page 16 of the TabPFN paper [1], on the OpenML-CC18, TabPFN has a better ROC Receiver Operating Characteristic after 1 second than XGboost, Catboost, LightGBM, KNN, SAINT, Reg. Cocktail, and Autogluon after any amount of time, but Auto-sklearn 2.0 required 5 minutes to reach ~ROC parity with TabPFN.

1. "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second" (May 2023) https://arxiv.org/abs/2207.01848

2. "Interpretable Graph Neural Networks for Tabular Data" (Aug 2023) https://arxiv.org/abs/2308.08945

[iAkashPaul]

Microsoft LIDA seems something that can incorporate this for additional insight from tabular data sources

[Artgor]

The paper sounds interesting, but without the code being available, it is difficult to say whether the idea is really viable.