I used to be in physics but theory, not experiment. I have experience at work with decision trees in a different field.

I've always thought that the idea that decision trees are "explainable" is very overstated. The moment that you go past a couple of levels in depth, it becomes an un-interpretable jungle. I've actually done the exercise of inspecting how a 15-depth decision trees makes decision, and I found it impossible to interpret anything.

In a neural network you can also follow the successive matrix multiplications and relu etc through the layers, but you end up not knowing how the decision is made.

Thoughts?