29. A computer program product embedded in a computer memory for updating at least one prediction model for use by at least one interactive server, wherein each interactive server performs a plurality of actions in the context of a plurality of input attribute values of an input dataset and wherein the actions are selected based on each prediction model, the computer program product comprising: (a) code for automatically and continually obtaining contextual data from the interactive server as it performs the plurality of actions, wherein the contextual data indicates at least which action was performed, which input attribute values are present for each action that was performed, and which outcome is achieved for each action was performed; (b) code for automatically and continually updating a learning model based on all of the obtained contextual data, wherein the learning model predicts a probability of each of a plurality of specific outcomes occurring for each of a plurality of specific actions being performed by the interactive server when specific combinations of one or more input attribute values are present, wherein the updating of the learning model is based on counts of each attribute value as it is present along with each of the other attribute values; (c) code for generating an updated prediction model, wherein generating comprises, determining a correlation between one or more values of input attributes and a target of prediction, comparing the correlation with a threshold, eliminating each value of input attributes from the input dataset if the value of input attribute fails to satisfy the threshold, and in response to eliminating, generating the updated prediction model based on the input dataset; (d) code for generating a prediction of a probability of an outcome using the updated prediction model; (e) code for selecting an action of the plurality of actions based on the prediction; and (f) code for performing the action, wherein operations (b) and (c) are performed each time condition is met, the condition being selected from a group consisting of (i) a predetermined level of contextual data has been obtained, (ii) a predetermined number of actions have been performed, (iii) a predetermine time period has expired, and (iv) a number of new input attributes from the collected contextual data has reached a predetermined percentage of a total number of the attributes or a predetermined minimum number of new input attributes has been reached.
29. A computer program product embedded in a computer memory for updating at least one prediction model for use by at least one interactive server, wherein each interactive server performs a plurality of actions in the context of a plurality of input attribute values of an input dataset and wherein the actions are selected based on each prediction model, the computer program product comprising: (a) code for automatically and continually obtaining contextual data from the interactive server as it performs the plurality of actions, wherein the contextual data indicates at least which action was performed, which input attribute values are present for each action that was performed, and which outcome is achieved for each action was performed; (b) code for automatically and continually updating a learning model based on all of the obtained contextual data, wherein the learning model predicts a probability of each of a plurality of specific outcomes occurring for each of a plurality of specific actions being performed by the interactive server when specific combinations of one or more input attribute values are present, wherein the updating of the learning model is based on counts of each attribute value as it is present along with each of the other attribute values; (c) code for generating an updated prediction model, wherein generating comprises, determining a correlation between one or more values of input attributes and a target of prediction, comparing the correlation with a threshold, eliminating each value of input attributes from the input dataset if the value of input attribute fails to satisfy the threshold, and in response to eliminating, generating the updated prediction model based on the input dataset; (d) code for generating a prediction of a probability of an outcome using the updated prediction model; (e) code for selecting an action of the plurality of actions based on the prediction; and (f) code for performing the action, wherein operations (b) and (c) are performed each time condition is met, the condition being selected from a group consisting of (i) a predetermined level of contextual data has been obtained, (ii) a predetermined number of actions have been performed, (iii) a predetermine time period has expired, and (iv) a number of new input attributes from the collected contextual data has reached a predetermined percentage of a total number of the attributes or a predetermined minimum number of new input attributes has been reached. 30. A computer program product as recited in claim 29 , wherein the contextual data is obtained from each of a plurality of interactive servers by a centralized model building server, wherein the learning model is updated by the centralized model building server to update the prediction model so that it is usable by any of the distributed interactive servers.