2016年10月27日 · This argument applies to other measures of error, not just to RMSE, but the RMSE is particularly attractive for direct comparison to the SD because their mathematical formulas are analogous. Edit: Someone asked me offline for a citation that supports the idea of the SD being a benchmark for the RMSE.

2013年4月17日 · The RMSE for your training and your test sets should be very similar if you have built a good model. If the RMSE for the test set is much higher than that of the training set, it is likely that you've badly over fit the data, i.e. you've created a model that tests well in sample, but has little predictive value when tested out of sample.

RMSE is a way of measuring how good our predictive model is over the actual data, the smaller RMSE the better way of the model behaving, that is if we tested that on a new data set (not on our training set) but then again having an RMSE of 0.37 over a range of 0 to 1, accounts for a lot of errors versus having an RMSE of 0.01 as a better model.

2017年3月2日 · RMSE (Root mean square error) and SD (Standard deviation) have similar formulas. This link says. The only ...

2024年4月25日 · Adding to Stephan Kolassa's answer, the most reasonable interpretation is this: Your xgboost an NN model were probably trained to give good mean predictions (e.g. if you used squared loss).

2022年6月1日 · The difference, and why the RMSE is commonly used and MRSE is not probably lies in interpretation of the terms and their related metrics. If we roll back RMSE to SE at every step of the way we get a term that is commonly used and interpretable. We square RMSE and get MSE (variance), we square root it and get SE.

2015年3月18日 · R-squared is conveniently scaled between 0 and 1, whereas RMSE is not scaled to any particular values. This can be good or bad; obviously R-squared can be more easily interpreted, but with RMSE we explicitly know how much our predictions deviate, on average, from the actual values in the dataset. So in a way, RMSE tells you more.

2017年2月8日 · RMSE is stated in the same units of the original measurement, so if you are comparing distance measuring techniques, you might have an RMSE of 0.29 meters. If you're measuring mountain height or river distances then this is a tiny amount of inaccuracy, perhaps 0.005%. If you're measuring people's height, then you're about 17% off.

2017年1月9日 · I think Euan has a right answer. There are ways to calculate the NRMSE, RMSE/(max()-min()) and RMSE/mean(). You should know which is better to be used in your case. For example, when you are calculating the NRMSE of a house appliance, it is better to use the RMSE/(max()-min()). Because in this way it can show the NRMSE when the appliance is ...

The second question is about RMSE and NRMSE. The merit of RMSE is to my mind largely that it is in the same units of measurement as the response variable. Statisticians and non-statisticians should find it relatively easy to think in terms of RMSE of 3.4 metres or …