RMSE - Root mean squared error
MSE - Mean squared error
MSE and RMSE are similar but behave differently for gradient based methods.
MAE - Mean absolute error
MAE optimal target constant is median - hence handles outliers better.
MSE optimal target constant is mean.
MAPE and MSPE are weighted versions of above.
P is for percentage.
They penalize based on the target absolute value.
For e.g. MSE and MAE will treat error of 1 equally for 9/10 and 999/1000.
But MAPE and MSPE won't. They will penalize more for 9/10 rather than
999/1000 since relative error is higher for 9/10.
Another one is RMSLE - uses logs. It penalizes relative errors and also unbiased towards smaller targets unlike MAPE and MSPE.
MSE - Mean squared error
MSE and RMSE are similar but behave differently for gradient based methods.
MAE - Mean absolute error
MAE optimal target constant is median - hence handles outliers better.
MSE optimal target constant is mean.
MAPE and MSPE are weighted versions of above.
P is for percentage.
They penalize based on the target absolute value.
For e.g. MSE and MAE will treat error of 1 equally for 9/10 and 999/1000.
But MAPE and MSPE won't. They will penalize more for 9/10 rather than
999/1000 since relative error is higher for 9/10.
Another one is RMSLE - uses logs. It penalizes relative errors and also unbiased towards smaller targets unlike MAPE and MSPE.
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