I can't explain it, but I help maintain TensorFlow Decision Forests [1] and Yggdrasil Decision Forests [2], and in an AutoML system at work that trains models on lots of various users data, decision forest models gets selected as best (after AutoML tries various model types and hyperparameters) somewhere between 20% to 40% of the times, systematically. It's pretty interesting. Other ML types considered are NN, linear models (with auto feature crossings generation), and a couple of other variations.
[1] https://github.com/tensorflow/decision-forests [2] https://github.com/google/yggdrasil-decision-forests