Facilitating machine learning in an online social network

Abstract

Automatic machine-learning processes and systems for an online social network are described. During operation of the online social network, a system can automatically collect labeled training events, obtain snapshots of raw entity data associated with subjects from the collected training events, produce training examples by generating features for each training event using the snapshots of entity data and current entity data, and split the training examples into a training set and a test set. Next, the system can use a machine-learning technique to train a set of models and to select the best model based on one or more evaluation metrics using the training set. The system can then evaluate the performance of the best model on the test set. If the performance of the best model satisfies a performance criterion, the system can use the best model to predict responses for the online social network.