# Need of Weighted Mean Squared Error

We have MSE and RMSE as evaluation metrics for regression problems. I have for some problems people use Weighted Mean Squared Error (WMSE) as the evaluation metrix.

Below is the WMSE formula: Can anyone explain me the real need of WMSE and when not to use MSE.

Just a note on your example: the used squared error ($$(predictedClicks - observedClicks)^2$$) is an absolute squared error (I would have expected a relative error), and therefore already increases with the number of clicks. So in this case, weighting that way reinforces the performance on cases with high number of clicks.