1. A method for employing a multiple output relaxation (MOR) machine learning model to predict, using an input, multiple interdependent output components of a multiple output dependency (MOD) output decision, each output component having multiple possible values, the method comprising: training a classifier for each of multiple interdependent output components of an MOD output decision to predict the output component based on the input and based on all of the other output components; initializing each of the possible values for each of the output components to a predetermined output value; running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the output components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached; and retrieving an optimal output component from each of the classifiers.