A loss aggregator is a machine learning method that combines outputs or predictions from multiple models to produce a single improved prediction. By aggregating model behavior, it can reduce variance and improve accuracy compared with relying on one model alone.
A type of machine learning algorithm that improves the performance of a model by combining the predictions of multiple models and using those predictions to make a single prediction. As a result, a loss aggregator can reduce the variance of the predictions and improve the accuracy of the predictions.