Data sampling for model exploration

A system, comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the system to:
• obtain a training dataset comprising a first set of records associated with a first set of identifier (ID) values for a first entity ID and an evaluation dataset comprising a second set of records associated with a second set of ID values for the first entity ID;
• select a random subset of ID values for the first entity ID from the second set of ID values;
• generate a sampled evaluation dataset comprising a first subset of records associated with the random subset of ID values in the second set of records;
• generate a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records;
• and
• output the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a first machine learning model comprising a global version and a first set of personalized versions for a first set of entities represented by the random subset of ID values.

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

• obtaining a training dataset comprising a first set of records associated with a first set of entity identifier (ID) values and an evaluation dataset comprising a second set of records associated with a second set of entity ID values;
• selecting a random subset of entity ID values from the second set of entity ID values;
• generating a sampled evaluation dataset comprising a first subset of records associated with the random subset of entity ID values in the second set of records;
• generating a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records;
• and
• outputting the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a machine learning model.