I would like to investigate interactions between my explanatory variables prior to building a statsitical model.
Apparently it is possible to do it in R using a regression tree (library: tree). This method is based on binary recursive partitioning. The algorithm predicts a response variable by partitioning the data into subgroups based on the predictor variables.
I do not understand, however, how this algorithm investigates interactions between predictors. I would be very grateful for an easy to understand explanation for non-expert.