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Loss Functions | MSE | MAE | RMSE

         Performance Metrics

The various metrics used to evaluate the results of the prediction are :

  • Mean Squared Error(MSE)
  • Mean Absolute error(MAE)
  • Root-Mean-Squared-Error(RMSE)
  • Adjusted R²

Mean Squared Error:
Mean Squared error is one of the most used metrics for regression tasks.
MSE is simply the average of the squared difference between the target value and value predicted by the regression model. 


As it squares the differences and penalizes (punish)even a small error which leads to over-estimation of how bad the model is.

It is preferred more than other metrics because it is differentiable and hence can be optimized better.



in the above formulae, y=actual value and (yhat) means predicted value by the model.


RMSE(Root Mean Squared Error:

This is the same as MSE (Mean Squared Error) but the root of the value is considered while determining the accuracy of the model.
It is preferred more in some cases because the errors are first squared before averaging which poses a high penalty on large errors. 
This implies that RMSE is useful when large errors are undesired.



Mean Absolute Error:

MAE is the Mean Absolute Error, The difference between the target value and value predicted by the model.
The MAE is more robust to outliers and does not penalize the errors like MSE.
The MAE takes an average of this error from every sample in a dataset and gives an output.
 



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