Self learning adaptive modeling system

摘要

Self-learning and adaptive modeling is employed with respect to predictive analytics. A hierarchical model structure can be employed comprising a set of predictive models automatically built from accumulated data and distributed across multiple levels. For a given input type, a set of candidate models can be identified across varying levels of granularity, and a best model selected based on a comparison of performance metrics of the models. The best model can then be activated for use in making predictions. Of course, the best model can change based on most recent training performance results, since as more data becomes available more specific models can be developed.

主权项

1. A method, comprising:
employing at least one processor configured to execute computer-executable instructions stored in a memory to perform the following acts: constructing a hierarchical predictive model comprising multiple predictive models across multiple levels of granularity; training each of the multiple predictive models based on accumulated data automatically at a predetermined interval; evaluating performance of each of the multiple predictive models after the training; and activating for use a root predictive model of the hierarchical predictive model and at least one child predictive model that outperforms a parent predictive model at a more specific level of granularity than the parent predictive model, wherein the parent predictive model is at an immediately preceding and more generic level in the hierarchy than the child predictive model.