Is it a good idea to compare RMSE?
Astur explains, there is no such thing as a good RMSE, because it is scale-dependent, i.e. dependent on your dependent variable. Hence one can not claim a universal number as a good RMSE. Even if you go for scale-free measures of fit such as MAPE or MASE, you still can not claim a threshold of being good.
Is higher RMSE better?
The RMSE is the square root of the variance of the residuals. Lower values of RMSE indicate better fit. RMSE is a good measure of how accurately the model predicts the response, and it is the most important criterion for fit if the main purpose of the model is prediction.
What does root mean square error ( RMSE ) really mean?
Root Mean Square Error (RMSE) is a standard way to measure the error of a model in predicting quantitative data. Formally it is defined as follows: Let’s try to explore why this measure of error… Get started
Why do we use RMSE as an estimator?
But then RMSE is a good estimator for the standard deviation σ of the distribution of our errors! We should also now have an explanation for the division by n under the square root in RMSE: it allows us to estimate the standard deviation σ of the error for a typical single observation rather than some kind of “total error”.
Why is RMSE not an appropriate score for probabilities?
Significance tests and model selection are subjects of intense disagreement between the Bayesians and the Frequentists. Triston’s comment about splitting the data-set into training and verification sets would not please a Bayesian. May I suggest that RMSE is not an appropriate score for probabilities.
Is the difference in MSE the same as the mean?
A difficulty compared to a standard test comparing two means is that your samples are correlated — both come from the same events. But a difference in MSE is the same as a mean of differenced squared errors (means are linear). This suggests calculating a one-sample t-test as follows: For each x compute a error e for procedure 1 and 2.