The tweets / medium article are a bit incomplete. But they mean at least: - Authors didn't make decent efforts to build a robust baseline. Worse it seems that they have purposefully built a bad baseline to make their solution look better. - Authors didn't really disclose full performance. Statistical performance is one thing but time complexity is another. Hopular needs 10 mins on a 500 rows datasets. That's a NO NO for any serious application. - Authors didn't provide an easy to use interface. You can't really claim SoTA on small tabular data with something that isn't testable by everyone.

I think it is notable because he's an expert in that area and would know how to assess it. I'm not a fan of the tone either though...

But the authors claim "Hopular surpasses Gradient Boosting (e.g. XGBoost), Random Forests, and SVMs on tabular data." So if a simple sklearn model works better, it's worth knowing it might not be all it's cracked up to be before deciding to spend hours understanding another paper.