Methods and Systems for Re-training a Machine Learning Model Using Predicted Features from Training Dataset

A server system, comprising:

• a communication interface;
• a memory comprising executable instructions;
• and
• a processor communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least:
• access a training feature set and a testing feature set from a database associated with the server system, the training feature set is associated with each training data sample in a training dataset and the testing feature set is associated with each testing data sample in a testing dataset;
• in response to determining an inclusion of at least one new feature in the testing feature set, train a surrogate Machine Learning (ML) model to predict a value corresponding to the at least one new feature based, at least in part, on the testing feature set;
• determine, by the surrogate ML model, a predicted value corresponding to the at least one new feature for each training data sample in the training dataset based, at least in part, on the training feature set;
• generate a new training feature set for each training data sample based, at least in part, on the corresponding predicted value and the corresponding training feature set;
• and
• re-train an ML model based, at least in part, on the new training feature set for each training data sample to obtain a re-trained ML model.

A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising:

• accessing a training feature set and a testing feature set from a database associated with the server system, the training feature set is associated with each training data sample in a training dataset and the testing feature set is associated with each testing data sample in a testing dataset;
• in response to determining an inclusion of at least one new feature in the testing feature set, training a surrogate Machine Learning (ML) model to predict a value corresponding to the at least one new feature based, at least in part, on the testing feature set;
• determining, by the surrogate ML model, a predicted value corresponding to the at least one new feature for each training data sample in the training dataset based, at least in part, on the training feature set;
• generating a new training feature set for each training data sample based, at least in part, on the corresponding predicted value and the corresponding training feature set;
• and
• re-training an ML model based, at least in part, on the new training feature set for each training data sample.