Contents
What is Rmsec?
RMSEC is measuring how well the calibration worked. It does NOT tell you how well it will predict future samples. RMSECV should be the result obtained by measuring an independent set of samples and does indicate the possible performance of your calibration.
What is a good RMSEP value?
Normalized Root Mean Squared Error (Norm RMSEP) A value greater than 2 is considered to be a good. There are also terms like, Standard Error of Prediction(SEP) and Ratio of the Standard Error of Prediction to Standard Deviation (RPD) which are mainly used in chemometrics.
How is Rmsep calculated?
RMSEP is calculated by summing all squared prediction errors during cross-validation and is an indicator of the reliability and predictive ability of the model. The lower the RMSEP value the higher is the predictive ability of the model.
What is root mean square error of calibration?
Root Mean Square Error (RMSE): A measure of the central tendency of errors from the validation series (estimated flow minus observed flow), equivalent to the Standard Error of the Estimate from the calibration. In a robust model, RMSE should be very similar to the Standard Error of the Estimate.
When to use rmsep instead of rmsecv?
RMSEP should be used if “new” data that the model was not build using, is used to predict. If you are just trying to explain the data and are not predicting new, unseen data then use RMSECV. RMSEP will always be greater than RMSECV.
What’s the difference between rmsec and are MSE?
(R)MSEC measures goodness of fit between your data and the calibration model. Depending on the type of data, model and application this can be subject to a huge optimistic bias due to overfitting compared to the (R)MSE observed for real cases when applying the calibration.
Why is there an optimistic bias in rmsep?
Depending on the type of data, model and application this can be subject to a huge optimistic bias due to overfitting compared to the (R)MSE observed for real cases when applying the calibration. If the model suffers from not being complex enough (underfitting), calibration error approximates prediction error.
Can a rmsep be used to measure performance?
RMSEP can measure e.g. how performance deteriorates over time (e.g. due to instrument drift), but only if the validation experiments have a design that allows to measure these influences. I’d recommend to report for both cross validation and prediction errors in detail how the test cases are set apart, and for what factors independence was ensured.