The starting point is intuitive I guess: I want to do a decision tree type of algorithm, but some of the variables are continuous, e.g. numbers instead of quantities like ages.
I cannot afford the computation to directly treat them as discrete variable, I also don't want to lose much information through binning, and I don't know which binning method is the best.
Maybe regression will be a good choice. But I don't want to use categorical variables to build the tree first then do the regression at leaves. I think treating both kinds of variables equally will be better. In other words, I wish continuous variables could also show up in the early branch. Of course, I guess it will be hard for tree structure to achieve this.
So I'm wondering if there's such hierarchy structure which can use both continuous and categorical variables as keys?