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 data D1 that comprises 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 that 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.