Can you compare RMSE?

Can you compare RMSE?

To phrase it another way, RMSE is a good way to answer the question: How far off should we expect our model to be on its next prediction? In your case, As far as I know, It’s not feasible to compare the RMSE across different subsets of data for model performance if that’s what you are doing.

What is RMSE in simple terms?

Root mean squared error (RMSE) is the square root of the mean of the square of all of the error. RMSE is a good measure of accuracy, but only to compare prediction errors of different models or model configurations for a particular variable and not between variables, as it is scale-dependent.

How does the RMSE model differ from the observed model?

For the sake of clarity for I mean: RMSE = sqrt ( (fitted-observed)^2/ n.observations ) The models differ one another for some indipendent variables which have differenent amounts of NA values (in particular since some variables represent the cumulative effect of others I have that the number of NAs increases the more variables I cumulate).

What should RMSE value be for linear regression?

We would be using linear regression to train our model, the data set used in my code can be downloaded from here: headbrain6 The RMSE value of our is coming out to be approximately 73 which is not bad. A good model should have an RMSE value less than 180.

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”.

How to calculate RMSE for training and test sets?

Now I fitted n-different models to the training set and calculated the RMSE on both the training and the test sets. From what I understand, the model having the lower RMSE in the test set should be the preferable one. For the sake of clarity for I mean: RMSE = sqrt ( (fitted-observed)^2/ n.observations )