1. A computer-implemented method and system for converting predictive models to equivalent models comprising the following steps: a) learning a model, M1, from dataset D; b) generating a model, B1, of the distribution of input variables in D; c) generating input patterns from B1 or D using statistical sampling; d) creating new dataset, D1, that consists of the generated inputs followed by the corresponding M1 model-estimated outputs; e) deriving all or multiple Markov Boundaries (MB 1 ,. .. , MB n ) of the response variable by application of appropriately instantiated TIE* method on D1; f) learning from each Markov Boundary (i.e., MB i ), an equivalent representation, DT i , which is easier to understand by humans or is easier to implement in practical application settings; g) verifying and fine tuning each equivalent model to capture the outputs of M1 within acceptable accuracy e; h) keeping only the Markov Boundaries that satisfy the condition of step g); and i) outputting the catalogue of all validated models DT i comprising the complete final set of equivalent explanations of the function contained in model M1.