A computer-implemented method for computing model outcomes in the course of an event, the event being described by values associated to variables, the method comprising: - providing (S102, S104) a first set of variables of a first set of observations describing similar events, the variables of the first set of variables being associated to each observation of the first set of observations, two observations of the first set of observations having different values for a respective variable of the first set of variables; - providing a multiplicity of models; - training the multiplicity of models based on the variables of the first set of observations, by: -- computing subsets of the observations of the first set by applying a filtering on the variables of the first set of observations, the filtering being based on the values of the variables of the observations; --training each model of the multiplicity of models on one of the subsets of the observations of the first set; - indexing each model of the multiplicity of models with the values of the variables of the subset of observations used to train the said each model; - collecting a second set of observations describing events that are similar to the events of the first set of observations, the observations of the second set comprising at least the same variables as the first set of observations, two observations of the second set of observations having different values for a respective variable of the second set of variables; - performing a query according to one or more variables of the second set of observations; - returning (S60, 70), as a result of the query, a model among the indexed models; - computing (S80, S90) an outcome with the returned model for an observation of the second set of observations. - providing (S102, S104) a first set of variables of a first set of observations describing similar events, the variables of the first set of variables being associated to each observation of the first set of observations, two observations of the first set of observations having different values for a respective variable of the first set of variables; - providing a multiplicity of models; - training the multiplicity of models based on the variables of the first set of observations, by: -- computing subsets of the observations of the first set by applying a filtering on the variables of the first set of observations, the filtering being based on the values of the variables of the observations; --training each model of the multiplicity of models on one of the subsets of the observations of the first set; -- computing subsets of the observations of the first set by applying a filtering on the variables of the first set of observations, the filtering being based on the values of the variables of the observations; --training each model of the multiplicity of models on one of the subsets of the observations of the first set; - indexing each model of the multiplicity of models with the values of the variables of the subset of observations used to train the said each model; - collecting a second set of observations describing events that are similar to the events of the first set of observations, the observations of the second set comprising at least the same variables as the first set of observations, two observations of the second set of observations having different values for a respective variable of the second set of variables; - performing a query according to one or more variables of the second set of observations; - returning (S60, 70), as a result of the query, a model among the indexed models; - computing (S80, S90) an outcome with the returned model for an observation of the second set of observations.