Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines output of a machine learning model, which includes a score generated by the model based on features inputted into the model and feature importance metrics representing effects of the features on the score. Next, the system maps the features to elements in a feature hierarchy that groups the features under a first level of parent features. The system also generates a ranking of the first level of parent features based on the feature importance metrics. The system then combines, based on the ranking, feature values of the mapped features with a set of insight templates to produce a list of narrative insights, wherein each narrative insight includes a natural language description of a factor that contributes to the model's output. Finally, the system outputs the list of narrative insights in a user interface.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.