Microsoft Corporation
MACHINE LEARNING TECHNIQUES TO IDENTIFY PREDICTIVE FEATURES AND PREDICTIVE VALUES FOR EACH FEATURE

Abstract:

Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.