Identifying optimal weights to improve prediction accuracy in machine learning techniques

A computer program product for improving prediction accuracy in machine learning techniques, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:

• constructing a teacher model, wherein said teacher model generates a weight for each data case;
• training a current student model using training data and weights generated by said teacher model;
• generating state features by said current student model;
• generating new weights by said teacher model using said state features;
• training a candidate student model using said training data and said new weights;
• generating a reward by comparing said current student model with said candidate student model using said training data and testing data to determine which is better at predicting an observed target;
• updating said teacher model with said reward in response to a stopping rule not being satisfied;
• and
• returning said new weights and said current student model to a user in response to said stopping rule being satisfied, wherein said returned student model provides a prediction of said observed target.

A system, comprising:

• a memory for storing a computer program for improving prediction accuracy in machine learning techniques;
• and
• a processor connected to said memory, wherein said processor is configured to execute the program instructions of the computer program comprising:
• constructing a teacher model, wherein said teacher model generates a weight for each data case;
• training a current student model using training data and weights generated by said teacher model;
• generating state features by said current student model;
• generating new weights by said teacher model using said state features;
• training a candidate student model using said training data and said new weights;
• generating a reward by comparing said current student model with said candidate student model using said training data and testing data to determine which is better at predicting an observed target;
• updating said teacher model with said reward in response to a stopping rule not being satisfied;
• and
• returning said new weights and said current student model to a user in response to said stopping rule being satisfied, wherein said returned student model provides a prediction of said observed target.