Model-specific synthetic data generation for machine learning model training

A system comprising:

• at least one hardware processor;
• and
• a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor to:
• receive a trained machine learning model,
• extract a set of features associated with said machine learning model, wherein each of said extracted features is assigned a feature importance score which represents a relative explanatory power of said feature with respect to an output of said machine learning model,
• generate a set of marginal queries based, at least in part, on a selected subset of said features having a highest said feature importance score,
• perform a measurement of said set of marginal queries on a source database, to obtain measurements of said set of marginal queries on said source database, and
• use said measurements to generate synthetic data that matches said measurements.

A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to:

• receive a trained machine learning model;
• extract a set of features associated with said machine learning model, wherein each of said extracted features is assigned a feature importance score which represents a relative explanatory power of said feature with respect to an output of said machine learning model;
• generate a set of marginal queries based, at least in part, on a selected subset of said features having a highest said feature importance score;
• perform a measurement of said set of marginal queries on a source database, to obtain measurements of said set of marginal queries on said source database;
• and
• use said measurements to generate synthetic data that matches said measurements.