Predicting user attributes using uncertainty estimate modeling

Abstract

Methods and apparatuses are described for predicting user attributes using uncertainty estimate modeling. A server trains a plurality of machine learning (ML) models to predict a distribution of values for a plurality of user attributes. The server determines an uncertainty measure of the ML models for each user attribute based upon the predicted distribution of values. The server receives a request for prediction of user attributes from a client device and generates for each user attribute a first predicted distribution using one or more of the trained models. The server classifies, for each user attribute, an accuracy of the first predicted distribution based upon the uncertainty measure and provides the first predicted distribution and the accuracy for each user attribute to the client device for presentation. The server updates the first predicted value for the user attributes based upon input received from the client computing device.