1. One or more computer storage media devices storing computer-useable instructions that, when used by one or more computing devices cause the one or more computing devices to perform a method for calculating event probabilities using linear trainable parameters that capture relationships and concepts that are regularly updatable for a quick incorporation of new data, the method comprising: identifying a request to calculate an event probability, wherein the event probability indicates an expected number of times the event will occur; associating information from the request with a set of feature groups, wherein the set of feature groups comprises a first subset of feature groups comprising linear trainable parameters characterized by consecutive integers, and a second subset of feature groups comprising non-linear trainable parameters, wherein a feature group is a classification of the information from the request, and wherein each of the first subset of feature groups includes a plurality of bins; associating a bin of the plurality of bins with the information from the request; identifying, by the one or more computing devices, counter information from at least an event counter and a non-event counter associated with the bin, wherein the event counter counts a number of event occurrences for the request and the non-event counter counts a number of non-event occurrences for the request; training, by the one or more computing devices, the event counter and the non-event counter using a linear-training algorithm; calculating, utilizing the counter information, the event probability; identifying and removing at least one non-billable traffic attribute; updating the counter information based on the removal of the at least one non-billable traffic attribute; and calculating, utilizing the updated counter information less the non-billable traffic, an updated event probability.