Apparatus and method for managing machine learning

A machine learning management apparatus comprising:

• a memory configured to store information on a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms using one or more training dataset sizes;
• and
• a processor configured to perform a procedure including:
• identifying a maximum prediction performance score amongst the prediction performance scores,
• calculating, for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score amongst the plurality of machine learning algorithms, based on execution results obtained by executing the first machine learning algorithm using the one or more training dataset sizes, first estimated prediction performance scores and first estimated runtimes for a case of executing the first machine learning algorithm using each of two or more training dataset sizes different from the one or more training dataset sizes, and determining, based on the maximum prediction performance score, the first estimated prediction performance scores, and the first estimated runtimes, a first training dataset size to be used when the first machine learning algorithm is executed next time, and
• calculating, for a second machine learning algorithm different from the first machine learning algorithm amongst the plurality of machine learning algorithms, based on execution results obtained by executing the second machine learning algorithm using the one or more training dataset sizes, second estimated prediction performance scores and second estimated runtimes for a case of executing the second machine learning algorithm using each of two or more training dataset sizes different from the one or more training dataset sizes, and determining, based on the maximum prediction performance score, the second estimated prediction performance scores, and the second estimated runtimes, a second training dataset size to be used when the second machine learning algorithm is executed next time.

A machine learning management method comprising:

• identifying, by a processor, a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms using one or more training dataset sizes;
• calculating, by the processor, for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score amongst the plurality of machine learning algorithms, based on execution results obtained by executing the first machine learning algorithm using the one or more training dataset sizes, first estimated prediction performance scores and first estimated runtimes for a case of executing the first machine learning algorithm using each of two or more training dataset sizes different from the one or more training dataset sizes, and determining, based on the maximum prediction performance score, the first estimated prediction performance scores, and the first estimated runtimes, a first training dataset size to be used when the first machine learning algorithm is executed next time;
• and
• calculating, by the processor, for a second machine learning algorithm different from the first machine learning algorithm amongst the plurality of machine learning algorithms, based on execution results obtained by executing the second machine learning algorithm using the one or more training dataset sizes, second estimated prediction performance scores and second estimated runtimes for a case of executing the second machine learning algorithm using each of two or more training dataset sizes different from the one or more training dataset sizes, and determining, based on the maximum prediction performance score, the second estimated prediction performance scores, and the second estimated runtimes, a second training dataset size to be used when the second machine learning algorithm is executed next time.