Several articles says that MAE is robust to outliers but MSE is not and MSE can hamper the model if errors are too huge. My question is that MSE and MAE both are error matrices, our priority is to just minimise the error whether we use MSE or MAE. Where does outliers come into play while using error matrices?
What difference can an error matrix make in linear regression for choosing optimal values of the parameters (in regards to outliers because as per my knowledge error matrices doesn't contribute for choosing parameters, its the loss function we are minimising) which we are trying to learn ($y=mx+c$ : parameters being $m$ and $c$)?
But if they are not helping with parameters then why are we worried about outliers while choosing error matrixes?