17. A method of employing a multiple output relaxation (MOR) machine learning model to predict multiple interdependent output components of a lead response management (LRM) multiple output dependency (MOD) output decision, each output component having multiple possible values, the method comprising: training a first classifier to predict a first of two interdependent output components of an LRM MOD output decision based on an input and based on the second output component; training a second classifier to predict the second of the two output components of the LRM MOD output decision based on the input and based on the first output component; 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.
20. The method as recited in claim 17 , wherein an equilibrium is reached when the difference between the output value of a possible value for an output component of a current iteration and the output value of a possible value for an output component of a previous iteration is less than or equal to a predetermined threshold value.