System and methods for machine learning training data selection

A method comprising:

• identifying a first amount of first target outputs that was used to train a first machine learning model and that resulted in a performance of the first trained machine learning model satisfying a threshold condition, wherein the first amount of first target outputs was part of first training data for the first machine learning model, the first training data comprising a first plurality of training data sets each including a subset of first training inputs and first target outputs;
• collecting, for a second machine learning model, second training data comprising a second plurality of training data sets each including a subset of second training inputs and second target outputs, the second training data collected until a second amount of second target outputs satisfies the first amount of first target outputs;
• and
• training the second machine learning model using the second training data collected until the second amount of second target outputs satisfies the first amount of first target outputs.

A system comprising:

• a memory component;
• and
• a processor communicatively coupled to the memory component, the processor to:
• run a plurality of simulation tests on a first machine learning model to obtain a plurality of results that are each produced during a respective simulation test, the first machine learning model gradually trained using first training data historically collected over a period of time, the first training data comprising a plurality of first training data sets each including a subset of first training inputs and first target outputs associated with one of a plurality of points in time during the period of time;
• determine a simulation test of the plurality of simulation tests at which corresponding results of the first machine learning model satisfy a threshold condition, wherein the threshold condition is based on historical data at a first point in time of the plurality of points in time;
• identify a first training data set of the plurality of first training data sets on which the first machine learning model used during the determined simulation test was trained, wherein the first training data set on which the first machine learning model used during the determined simulation test was trained is associated with one or more second points in time that precede the first point in time;
• and
• determine a subset of target outputs from the identified first training data set on which the first machine learning model used during the determined simulation test was trained, the determined subset of first target outputs to define an amount of second training data to be sufficient to train a second machine learning model.