Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features. Next, the system obtains, from the feature configurations, an anchor containing metadata for accessing a first feature in an environment and a feature derivation for generating a second feature from the first feature. The system then uses the anchor to retrieve feature values of the first feature from the environment and uses the feature derivation to generate additional feature values of the second feature from the feature values of the first feature. Finally, the system provides the additional feature values for use with one or more machine learning models.

Abstract: The disclosed embodiments provide a method and system for processing data. During operation, the system obtains a dependency graph associated with feature selection in a statistical model, wherein nodes in the dependency graph include one or more feature sources, one or more transformers, and an assembler. Next, the system uses the dependency graph to derive an evaluation order associated with the nodes. The system then compiles a set of configurations for the statistical model according to the evaluation order.

Abstract: The disclosed embodiments provide a system and method for processing data. The system includes a model compiler that obtains a first configuration for a statistical model. The first configuration may include one or more compilation parameters associated with feature selection in the statistical model. Next, the model compiler uses the compilation parameter(s) and a first set of input features for the first configuration to generate a first feature subset for use with the statistical model and include the first feature subset in a first compiled form of the first configuration. The system also includes an execution engine that uses the first compiled form to execute the statistical model.

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.

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.