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Are there (complex) tabular datasets where deep neural networks (e.g. more than 3 layers) outperform traditional methods such as XGBoost by a large margin?

I'd prefer tabular datasets rather than image datasets, since most image dataset are either too simple that even XGBoost can perform well (e.g. MNIST), or too difficult for XGBoost that its performance is too low (e.g. almost any dataset that is more complex than CIFAR10; please correct me if I'm wrong).

nbro
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Clara
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In my opinion, no. Also images could be interpreted as tabular dataset as well, where certain columns represent different rgb codes of pixels. If you seek to use neural nets opt for image datasets, with large sample size. Neural networks generally require large sample sizes to perform, and huge dimension inputs to not be outperformed by boosting.

MatthewTuby
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