Regression model using combination of ranges/parts

My main goal is making predictions using a nonlinear model that have many independent variables.

I would like to split my numerical independent variables into ranges/parts. Then to use a combination of these ranges to predict a dependent variable. Suppose I split each variable A, B and C into 4 ranges. I will have 4^3 combinations. Is there any type of regression that do the optimal splitting and give results for each combination ?

• Why do you want to split? – user2974951 Feb 15 '19 at 10:52
• Suposse I have a numerical independent variable that goes from 100 to 200. I want to do like 100-125 -> 1 . 125-150->2 . 150-175->3 . 175-200->4 . In this way I can use the numbers as factors and the combine with other factors. Thanks. – Pedro Serra Feb 15 '19 at 11:14
• Have a look at stats.stackexchange.com/questions/68834/… for why that is a bad idea. – user2974951 Feb 15 '19 at 11:45

"Given a regression model, segmented $$`$$updates' the model by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed."