Computer scienceData scienceMachine learningRegressionRegression metrics

MSE and RMSE

Score interpretation

Report a typo

We know that the ideal value for the discussed loss functions would be 0, meaning our model captured the data perfectly. However, this is not a realistic scenario.

Suppose we made three regression models and calculated the nRMSE for each one of them, with the following scores: $\text{nRMSE}_{M_1} \approx 0.123$ , $\text{nRMSE}_{M_2} = 0.0325$ , and $\text{nRMSE}_{M_3} \approx 0.0574$ .

Which conclusion(s) can be reached based on the obtained scores?

Select one or more options from the list

The second model outperformed the other two, making it the best model out of the 3 we've built

We can confidently say that the second model is the best possible predictor for our dataset

The first model demonstrated the worst performance out of the 3 models we've built

The second model has reached the perfect score

___

Create a free account to access the full topic