When does deep learning outperform GBDT on tabular data?

Gradient Boosting Decision Trees (GBDT) are traditionally considered very effective for tabular data, but deep learning can outperform it in certain cases:

• Large volume of data: with a large number of training examples, neural networks can better identify complex dependencies.
• Complex, high-dimensional features: if data contains many features with nonlinear interactions, deep models can model them better.
• Presence of additional data sources: for example, if textual or visual data are added to tabular data, deep learning can integrate them into a single model.
• Automatic feature extraction: deep models can automatically create new data representations, sometimes surpassing manual feature selection.

However, for most classical tabular data tasks, GBDT remains a simpler and more effective solution. Deep learning wins when data and tasks are more complex and require flexibility in modeling.