Machine Learning, Python, Data Science, Artificial Intelligence, Deep Learning.
between the predicted and actual values. Unlike MSE, MAE does not square the errors, which makes it less sensitive to outliers. Use MAE when you want to focus on the magnitude of errors without considering their direction. Pros : 1. Robust to outliers. 2. It will be in the same unit. Cons : 1.