its all much of a muchness. You can use a linear function at the nodes to project the space before applying a >. However, all these tweaks tend to just consume

its all much of a muchness. You can use a linear function at the nodes to project the space before applying a >. However, all these tweaks tend to just consume CPU for marginal benefit. You can still appoximate a non axis aligned decision boundary with a series of stepwise aligned splits, albeit less compactly. But if your aim is to just generate a ton of non linear distance functions ... a la random forest, focusing on tree expressivity is the wrong thing. Why not spend your CPU cycles on more trees instead instead, which leads to smoother decision boundaries in general when mixing their responses. There is a ton of tweaks you can do, but unless you are expressly researching ML algorithms, the pragmatist should not care and just try a few options from whatever comes in scipy.