humans provide accidental regularization - and a human made decision boundary can have further fine-tuning by an expert. This is important related to things lik

humans provide accidental regularization - and a human made decision boundary can have further fine-tuning by an expert. This is important related to things like calibrating probabilities, which decision trees do very poorly on even if they get good scores on the metric they're trained on. Even better if you have a generative model which can "hallucinate" parts of the decision boundary which don't have points. Now you can "probe" your model and have a human intervene if they think parts of the decision boundary are wrong I suppose a human can modify a decision tree as well - but in practice human generated rules may be better for some domains.