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.
21. The method as recited in claim 17 , where one or more additional classifiers is trained to predict one or more additional interdependent output components of the LRM MOD output decision based on the input and based on the first output component, the second output component, and each other additional output component; the input, the second output component, and each additional output component are used to predict the first output component; and the input, the first output component, and each additional output component are used to predict the second output component.