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.
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. 4. The method as recited in claim 1 , wherein: the MOD output decision is an LRM MOD output decision; the input comprises an input feature vector that includes constant features about a lead and interactive features related to interactions between an agent and the lead, the features of the input feature vector including one or more of lead source, lead title, lead industry, lead state, lead created date, lead company size, lead status, number of previous dials, number of previous emails, previous action, hours since last action, response agent title, response method, response message type, response timing, agent or lead demographic profile, agent or lead histographic profile, agent or lead psychographic profile, agent or lead social network profile, agent or lead geographic profile, response frequency, response persistence, and data on current events; and the output components include one or more of response agent title, response method, response message type, response timing, agent or lead demographic profile, agent or lead histographic profile, lead contact title, agent or lead psychographic profile, agent or lead social network profile, agent or lead geographic profile, response frequency, and response persistence.