Neural networks vs gradient boosting: which is better for tabular data?

Gradient boosting algorithms such as XGBoost or LightGBM outperform most neural networks on typical tabular datasets due to their ability to handle missing data and their reduced need for tuning. Thus, for most business/EDA tabular tasks, use gradient boosting; try neural networks if the dataset is huge or includes mixed types (images, text, etc.).