17. A system that facilitates an automatic machine-learning process for an online social network, comprising: a computing system including a processor and a memory; wherein the computing system is configured to run an online social network; and wherein the online social network is configured to, automatically collect labeled training events; snapshot raw entity data associated with subjects from the collected training events; generate features for each training event using the snapshotted entity data and the current entity data to produce training examples; consolidate the training examples from one or more time periods to produce a consolidated set of training examples; determine 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; resolve the contradictory user actions for the consolidated set of training examples; split the training examples into a training set and a test set; use 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; evaluate the performance of the best model on the test set; and if the performance of the best model satisfies a performance criterion, use the best model to predict responses for the online social network.
17. A system that facilitates an automatic machine-learning process for an online social network, comprising: a computing system including a processor and a memory; wherein the computing system is configured to run an online social network; and wherein the online social network is configured to, automatically collect labeled training events; snapshot raw entity data associated with subjects from the collected training events; generate features for each training event using the snapshotted entity data and the current entity data to produce training examples; consolidate the training examples from one or more time periods to produce a consolidated set of training examples; determine 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; resolve the contradictory user actions for the consolidated set of training examples; split the training examples into a training set and a test set; use 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; evaluate the performance of the best model on the test set; and if the performance of the best model satisfies a performance criterion, use the best model to predict responses for the online social network. 21. The system of claim 17 , wherein while evaluating the best model the online social network is configured to use quantitative and qualitative measures; and wherein using a qualitative measure includes using a crowd-sourcing evaluation technique that comprises: sending a selected set of training examples and associated responses that were produced by the best model to a crowd-sourcing module that solicits the opinions of an additional set of users about the accuracy of the responses; and comparing the predictions of the best model with predictions of the additional set of users on the selected training examples.