1. A binary tree-structured classification and prediction method for supervised learning on complex datasets where a plurality of predictors determine an outcome, the method comprising the steps of: a) transforming the predictors if the predictors are not quantitative; b) transforming the outcome with optimal scoring; c) regressing the transformed outcome with optimal scoring on the transformed predictors according to a node specific variable ranking of each of the transformed predictors; d) repeat step c) for subsets of the transformed predictors, from least significant to most, until only one single transformed predictor remains; e) cross-validating nested families of the transformed predictors produced by step d); f) selecting an optimal subset of the transformed predictors based on step e); g) defining a binary splitting criterion using the optimal subset; h) determining whether a significant association exists between the outcome and the transformed predictors in the optimal subset; and i) repeat step c) if the significant association exists.