Why Gradient Boosted Decision Trees are so underappreciated in the industry?

GBDT allow you to iterate very fast, they require no data preprocessing, enable you to incorporate business heuristics directly as features, and immediately show if there is explanatory power in features in relation to the target.

On tabular data problems, they outperform Neural Networks, and many use cases in the industry have tabular datasets.

Because of those characteristics, [they are winning solutions to all tabular competitions on Kaggle](https://jobs-in-data.com/blog/data-science-skills#sota-ml-models).

And yet, somehow they are not very popular.

On the chart below, …

business cases data data preprocessing datascience decision decision trees features gradient heuristics industry iterate networks neural networks power show tabular tabular data trees use cases