Why do tree-based models still outperform deep learning on tabular data?

Why tree models often beat deep learning on table data

Deep nets changed how we read images and text, but on spreadheets and tables, simple tree methods still win more often.
A big test across many datasets found that tree-based models like XGBoost and Random Forests tend to do better on medium-size tables around ten thousand rows, even when we try hard to tune neural networks.
The teams ran lots of checks and lots of settings, and the pattern keeps coming back.
Why? Trees handle useless columns, keep the data shape, and can learn odd patterns more easily.
This shows that deep learning is not a one-size-fits-all tool, and we need new ideas for table data.
The authors also share a big set of tests so others can try, its raw results and settings.
That should help engineers build better models that are robust to messy inputs.
In short, when your data sits in rows and columns, dont assume deep nets will be best, trees might still be smarter for that task.

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Why do tree-based models still outperform deep learning on tabular data?

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