The purpose of this webservice is to infer parameters of an ELECTRE TRI Bouyssou-Marchant model, also called MR-Sort model. It takes in input the categories and criteria of the model and examples of assignments. The user should also specify several parameters of the metaheuristic: the size of the population of models (nmodels), the number of iterations to do with the heuristic adjusting the profiles, and the number of iterations (niter_heur) of the main loop (niter_meta). The metaheuristic will try to find a MRSort model which is compatible with the highest number of examples given in input.Olivier Sobrie, Vincent Mousseau, Marc Pirlot: Learning a Majority Rule Model from Large Sets of Assignment ExamplesThe list of criteria of the model.The list of learning alternatives to use to learn the MR-Sort model.The list of categories of the model and their rankThe performance table of the learning alternatives.The assignments of the alternatives in the different categories.The parameters of the metaheuristic. 'nmodels': the size of the population; 'niter_heur': the number of iterations of the heuristic; 'niter_meta': the number of iterations of the main loop.The solver to use. Currently CPLEX and GLPK are supported. In none specified, GLPK is used.