9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for facilitating an automatic machine-learning process for an online social network, the method comprising: during operation of the online social network, automatically collecting labeled training events; snapshotting raw entity data associated with subjects from the collected training events; generating features for each training event using the snapshotted entity data and the current entity data to produce training examples; consolidating the training examples from one or more time periods to produce a consolidated set of training examples; determining one or more contradictory user actions upon a set of entity data in the consolidated set of training examples, wherein a contradictory user action involves a same user responding differently to the same entity data over different time intervals; resolving the contradictory user actions for the consolidated set of training examples; splitting the training examples into a training set and a test set; using a machine-learning technique to train a set of models and select the best model based on one or more evaluation metrics using the training set; evaluating the performance of the best model on the test set; and if the performance of the best model satisfies a performance criterion, using the best model to predict responses for the online social network.
9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for facilitating an automatic machine-learning process for an online social network, the method comprising: during operation of the online social network, automatically collecting labeled training events; snapshotting raw entity data associated with subjects from the collected training events; generating features for each training event using the snapshotted entity data and the current entity data to produce training examples; consolidating the training examples from one or more time periods to produce a consolidated set of training examples; determining one or more contradictory user actions upon a set of entity data in the consolidated set of training examples, wherein a contradictory user action involves a same user responding differently to the same entity data over different time intervals; resolving the contradictory user actions for the consolidated set of training examples; splitting the training examples into a training set and a test set; using a machine-learning technique to train a set of models and select the best model based on one or more evaluation metrics using the training set; evaluating the performance of the best model on the test set; and if the performance of the best model satisfies a performance criterion, using the best model to predict responses for the online social network. 10. The non-transitory computer-readable storage medium of claim 9 , further comprising repeating the method to continually update a model that predicts responses for the online social network.