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17 de abr. de 2013 · 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.
27 de oct. de 2016 · Let's say I have a model that gives me projected values. I calculate RMSE of those values. And then the standard deviation of the actual values. Does it make any sense to compare those two values (variances)? What I think is, if RMSE and standard deviation is similar/same then my model's error/variance is the same as what is actually going on.
8 de feb. de 2017 · 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.
9 de ene. de 2017 · 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 running. The reason why your mean value is 0 could be the data has both positive part and negative part, therefore, I think RMSE/(max()-min()) can show how your data ...
18 de jul. de 2017 · If your response (temperature of the next day) is in degrees Celsius, and your RMSE is 47, then the units of that 47 is degrees Celsius. $\endgroup$ – Gregor Thomas Commented Jul 18, 2017 at 7:25
14 de feb. de 2015 · How to report RMSE of Lasso using glmnet in R. 2. Calculating RMSEC and RMSECV of PCA in R. 2.
19 de nov. de 2020 · It's almost impossible to get equal RMSE for test and train data. If it is not equal, then based on the above rule, it is always overfit or underfit. I also read that RSME is good or bad depends on the dependent variable (DV) range. Example if RMSE is 300 and if the range of DV is 20 to 100000, this is considered small?
12 de oct. de 2018 · As the RMSE is in log-space it behaves like a multipicative factor. So you are finding the square root of the mean of the squared ratio between the model values and the true values. I.e. if the RMSE were 0.693 (=ln 2) the model values would be roughly a factor of two out on average (in either direction) from the true values in the original (non-log) space.
$\begingroup$ Since the RMSE is calculated as sqrt(RSS/n-k) and RSS=sum[(y-hat(y)^2], it is calculating the entire regression model's RMSE. hat(y) is the predicted y, and you already have y in your data. $\endgroup$ –
13 de abr. de 2023 · mse值和rmse值受异常值残差影响较大，因此可使用平均绝对误差mae（又称l1范数损失），即误差绝对值的平均值。 MAE可以准确反映实际预测误差的大小。 MAE用于评价真实值与拟合值的偏离程度，MAE值越接近于0，说明模型拟合越好，模型预测准确率越高（但是RMSE值还是使用最多的）。
