RMS error (RMSE), average absolute error (MAE), standard deviation (Deviation) comparison

RMSE Root Mean square error, which is the square root of the sum of the squares of the observed and truth deviations and the m ratio of the observed times. is used to measure the deviation between the observed value and the truth.

MAE Mean Absolute error, the mean absolute error is the actual condition that the mean of absolute error can better reflect the error of the predicted value.

Standard deviation Standard Deviation, which is the mean square root of variance, is used to measure the degree of dispersion of a group of numbers themselves.

Rmse vs. Standard deviation: The standard deviation is used to measure the degree of dispersion of a group of numbers themselves, and the RMS error is used to measure the deviation between the observed and true values , They are subject to different research objectives, but the computational process is similar.

Rmse versus Mae: Rmse equals L2 norm, mae equals L1 norm. The higher the number of times, the more the calculation results are related to the larger values, and the smaller values are ignored, so this is why RMSE is sensitive to the exception value (i.e. there is a large difference between a predicted value and the real one, then the RMSE will be large).