Loss Function, Data Science, Heartbeat, Data Science For Ml.
performance. MAE loss is useful if the training data is corrupted with outliers (i.e. we erroneously receive unrealistically huge negative/positive values in our training environment, but not our testing environment). Intuitively, we can think about it like this: If we only had to give one