Ensemble averaging (machine learning)

Ensemble averaging is a way to combine the results from multiple different machine learning models. It is used to make predictions that are more accurate, reliable, and robust than those from any single model. That’s because models that are trained differently, with different data or parameters, will often come up with different answers. The idea with ensemble averaging is to take these multiple models and average the results together to get a more accurate answer.