additionally trees have fewer, often computably optimal parameters, whereas DNN often require extensive hyperparameter tuning and even neural architecture search(how many rows, what activations, what optimization method...). Furthermore, trees are generally more interpretable. There have been some recent interesting papers relating random forest trees to adaptive smoothers, that tries to understand why they beat dnns on tabular data.