Cascaded, boosted predictive models trained using distinct sets of exogenous and endogenous features are configured to predict component of performance ratings of entities. From the distinct predicted components, the second entity's rating factor can be determined. A second entity's rating factor represents the specific contribution a second entity makes to his average performance rating, as distinct from the rating that an arbitrary or hypothetical second entity would obtain.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning system, comprising: one or more processors; an event data store comprising a plurality of historical event records, each event record characterizing attributes of an observed event associated with a first entity and a second entity, each event record comprising: a plurality of observed exogenous variables associated with the first entity, the second entity and the event observed prior to the start of the event; a plurality of observed endogenous variables associated with the first entity, the second entity and the event observed after the start of the event; a plurality of elicited endogenous variables associated with the first entity, the second entity and the event elicited from the second entity or the first entity during or after the event; an observed dependent variable for the second entity provided by the first entity for the event after the event is completed; one or more memory resources storing instructions that, when executed by the one or more processors, cause the one or more processors to generate a set of cascaded, boosted predictive models of components of the dependent variable using the variables from the historical event records from the event data store, the predictive models comprising: a first predictive model configured to predict a first, expected component of a predicted output variable for an arbitrary second entity, wherein the observed dependent variable is the dependent variable of the first predictive model, and the observed exogenous variables associated with the event are the independent variables of the first predictive model; a second predictive model configured to predict a second component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable and the first, expected component is the dependent variable for the second predictive model, and the plurality of observed endogenous variables associated with the event are the independent variables of the second predictive model; and a third predictive model configured to predict a third component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable, and the sum of the first and second components is the dependent variable for the third predictive model, and the plurality of elicited endogenous variables associated with the event are the independent variables of the third predictive model. 2. The system of claim 1, wherein the instructions further cause the one or more processors to: determine for each second entity a contribution factor to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events by the second entity and the first expected component of the predicted output variable predicted by the first predictive model for each of the historical events, and store the second entity's contribution factor in a second entity record; determine for each second entity a second component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the second component of the predicted output variable predicted by the second predictive model for each of the historical events; and determine for each second entity a third component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the third component of the predicted output variable predicted by the third predictive model for each of the historical events. 3. The system of claim 1, wherein: an event record includes a plurality of experimental endogenous variables associated with the first entity, the second entity and the event; the instructions further cause the one or more processors to: generate a fourth predictive model configured to predict a fourth, experimental component of the predicted output variable rating for the arbitrary second entity, wherein a residual between the observed dependent rating and the first, second, and third predicted components is the dependent variable for the fourth predictive model, and the plurality of experimental endogenous variables associated with the independent variables of the fourth predictive model; and determine for each second entity a fourth component of to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events and fourth component of the predicted variable predicted by the fourth predictive model for each of the historical events. 4. The system of claim 1, wherein the instructions further cause the one or more processors to: automatically transmit one or more messages to a second entity during an event, wherein the messages contain instructions for behaviors by the second entity to improve a likelihood of the second entity receiving a higher performance rating at the completion of the event; store a set of second entity screening criteria indicating threshold probability levels for a second entity to obtain a target average performance rating by a target number of rated events provided by the second entity, wherein the threshold probability levels are determined with respect to a set of second entities having values of the performance contribution factor below a predetermined level, and wherein the second entity screening criteria include a minimum probability threshold value and a maximum probability threshold value determine for a second entity, based on number of events made by the second entity and the second entity's average performance rating, the probability of the second entity obtaining the target average performance rating by the target number of events; compare the determined probability for the second entity with the second entity screening criteria; and responsive to the determined probability for the second entity being between the minimum and maximum threshold probability values of the second entity screening criteria, storing an indication for the messaging module to automatically transmit at least one message to a rider during a subsequent event made by the second entity. 5. The system of claim 1, wherein each of the predictive models is a gradient boosted decision tree classifier. 6. A machine learning method, comprising: storing in an event data store a plurality of historical event records, each event record characterizing attributes of an observed event associated with a first entity and a second entity, each event record comprising: a plurality of observed exogenous variables associated with the first entity, the second entity and the event observed prior to the start of the event; a plurality of observed endogenous variables associated with the first entity, the second entity and the event observed after the start of the event; a plurality of elicited endogenous variables associated with the first entity, the second entity and the event elicited from the second entity or the first entity during or after the event; an observed dependent variable for the second entity provided by the first entity for the event after the event is completed; and generating a set of cascaded, boosted predictive models of components of the dependent variable using the variables from the historical event records from the event data store, the predictive models comprising: a first predictive model configured to predict a first, expected component of a predicted output variable for an arbitrary second entity, wherein the observed dependent variable is the dependent variable of the first predictive model, and the observed exogenous variables associated with the event are the independent variables of the first predictive model; a second predictive model configured to predict a second component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable and the first, expected component is the dependent variable for the second predictive model, and the plurality of observed endogenous variables associated with the event are the independent variables of the second predictive model; and a third predictive model configured to predict a third component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable, and the sum of the first and second components is the dependent variable for the third predictive model, and the plurality of elicited endogenous variables associated with the event are the independent variables of the third predictive model. 7. The method of claim 6, further comprising: determining for each second entity a contribution factor to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events by the second entity and the first expected component of the predicted output variable predicted by the first predictive model for each of the historical events, and store the second entity's contribution factor in a second entity record; determining for each second entity a second component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the second component of the predicted output variable predicted by the second predictive model for each of the historical events; and determining for each second entity a third component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the third component of the predicted output variable predicted by the third predictive model for each of the historical events. 8. The method of claim 6, wherein: an event record includes a plurality of experimental endogenous variables associated with the first entity, the second entity and the event; the method further comprising: generating a fourth predictive model configured to predict a fourth, experimental component of the predicted output variable rating for the arbitrary second entity, wherein a residual between the observed dependent rating and the first, second, and third predicted components is the dependent variable for the fourth predictive model, and the plurality of experimental endogenous variables associated with the event the independent variables of the fourth predictive model; and determining for each second entity a fourth component of to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events and fourth component of the predicted variable predicted by the fourth predictive model for each of the historical events. 9. The method of claim 6, further comprising: automatically transmitting one or more messages to a second entity during a event, wherein the messages contain instructions for behaviors by the second entity to improve a likelihood of the second entity receiving a higher performance rating at the completion of the event; storing a set of second entity screening criteria indicating threshold probability levels for a second entity to obtain a target average performance rating by a target number of rated events provided by the second entity, wherein the threshold probability levels are determined with respect to a set of second entities having values of the performance contribution factor below a predetermined level, and wherein the second entity screening criteria include a minimum probability threshold value and a maximum probability threshold value determining for a second entity, based on number of events made by the second entity and the second entity's average performance rating, the probability of the second entity obtaining the target average performance rating by the target number of events; comparing the determined probability for the second entity with the second entity screening criteria; and responsive to the determined probability for the second entity being between the minimum and maximum threshold probability values of the second entity screening criteria, storing an indication for the messaging module to automatically transmit at least one message to a rider during a subsequent event made by the second entity. 10. The method of claim 6, wherein each of the predictive models is a gradient boosted decision tree classifier. 11. A computer program product, comprising: a computer readable medium non-transitorily storing instructions, when executed by one or more processors, cause the one or more processors to: store in an event data store a plurality of historical event records, each event record characterizing attributes of an observed event associated with a first entity and a second entity, each event record comprising: a plurality of observed exogenous variables associated with the first entity, the second entity and the event observed prior to the start of the event; a plurality of observed endogenous variables associated with the first entity, the second entity and the event observed after the start of the event; a plurality of elicited endogenous variables associated with the first entity, the second entity and the event elicited from the second entity or the first entity during or after the event; an observed dependent variable for the second entity provided by the first entity for the event after the event is completed; generate a set of cascaded, boosted predictive models of components of the dependent variable using the variables from the historical event records from the event data store, the predictive models comprising: a first predictive model configured to predict a first, expected component of a predicted output variable for an arbitrary second entity, wherein the observed dependent variable is the dependent variable of the first predictive model, and the observed exogenous variables associated with the event are the independent variables of the first predictive model; a second predictive model configured to predict a second component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable and the first, expected component is the dependent variable for the second predictive model, and the plurality of observed endogenous variables associated with the event are the independent variables of the second predictive model; and a third predictive model configured to predict a third component of the predicted output variable for the arbitrary second entity, wherein a residual between the predicted output variable, and the sum of the first and second components is the dependent variable for the third predictive model, and the plurality of elicited endogenous variables associated with the event are the independent variables of the third predictive model. 12. The computer program product of claim 11, wherein the instructions further cause the one or more processors to: determine for each second entity a contribution factor to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events by the second entity and the first expected component of the predicted output variable predicted by the first predictive model for each of the historical events, and store the second entity's contribution factor in a second entity record; determine for each second entity a second component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the second component of the predicted output variable predicted by the second predictive model for each of the historical events; and determine for each second entity a third component of the second entity's historical predicted variable based on the aggregated difference between the historical predicted variables for the second entity for a plurality of historical events and the third component of the predicted output variable predicted by the third predictive model for each of the historical events. 13. The computer program product of claim 11, wherein: an event record includes a plurality of experimental endogenous variables associated with the first entity, the second entity and the event; the instructions further cause the one or more processors to: generate a fourth predictive model configured to predict a fourth, experimental component of the predicted output variable rating for the arbitrary second entity, wherein a residual between the observed dependent rating and the first, second, and third predicted components is the dependent variable for the fourth predictive model, and the plurality of experimental endogenous variables associated with the independent variables of the fourth predictive model; and determine for each second entity a fourth component of to the second entity's historical predicted variable based on the aggregated differences between the historical predicted variables for the second entity for a plurality of historical events and fourth component of the predicted variable predicted by the fourth predictive model for each of the historical events. 14. The computer program product of claim 11, wherein the instructions further cause the one or more processors to: automatically transmit one or more messages to a second entity during an event, wherein the messages contain instructions for behaviors by the second entity to improve a likelihood of the second entity receiving a higher performance rating at the completion of the event; store a set of second entity screening criteria indicating threshold probability levels for a second entity to obtain a target average performance rating by a target number of rated events provided by the second entity, wherein the threshold probability levels are determined with respect to a set of second entities having values of the performance contribution factor below a predetermined level, and wherein the second entity screening criteria include a minimum probability threshold value and a maximum probability threshold value determine for a second entity, based on number of events made by the second entity and the second entity's average performance rating, the probability of the second entity obtaining the target average performance rating by the target number of events; compare the determined probability for the second entity with the second entity screening criteria; and responsive to the determined probability for the second entity being between the minimum and maximum threshold probability values of the second entity screening criteria, storing an indication for the messaging module to automatically transmit at least one message to a rider during a subsequent event made by the second entity. 15. The computer program product of claim 11, wherein each of the predictive models is a gradient boosted decision tree classifier.