This past week I came across another paper that purports to get the SOTA for NNs for tabular data. Due to the extreme penchant for exaggeration in this community, I have given up on checking most of these claims, but decided to take a look at this particular work.
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I decided to check how does XGBoost *really* perform on the datasets used in the paper, and the results were not pretty.
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2/6
The main takeaway: for all three datasets used in the paper, the reported performance of XGBoost was widely inaccurate and the real performance was much better than their best results.
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3/6
This lack of careful considerations is unfortunately extremely prevalent in the “NNs for tabular data research community.”
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One gets a feeling that whole community has an actual disdain for tabular data, and a contempt to do all the important and necessary background work to get a better understanding of their subject matter.
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5/6
Read more in my latest Medium post: @tunguz/another-deceptive-nn-for-tabular-data-the-wild-unsubstantiated-claims-about-constrained-f9450e911c3f" target="_blank" rel="noopener" onclick="event.stopPropagation()">medium.com
Three XGBoost notebooks that I used can be found below:
Loan dataset: github.com
Blog dataset: github.com
COMPAS dataset: github.com
#DataScience #MachineLearning #DS #ML #AI
6/6
Three XGBoost notebooks that I used can be found below:
Loan dataset: github.com
Blog dataset: github.com
COMPAS dataset: github.com
#DataScience #MachineLearning #DS #ML #AI
6/6
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