I have been reading a book about Decision Trees and it caught my attention the following part:
In case of numeric attributes, decision trees can be geometrically interpreted as a collection of hyperplanes, each orthogonal to one of the axes
I tried to look for information about this in Internet, but I did not find anything. To what the author refers in this point?