The results in Table 2 indicate the Gaussian Naïve Bayes algorithm (with no calibration) is good enough to filter raw data with large errors. A significant reduction of the standard deviation value of relative error\(\sigma_{\text{Rev}}\) from ±20.3% to ±8.0 % shows the Naïve Bayes method is effective to reduce the random errors. However, it may not be useful to correct the systematic error, as the mean relative error\(\overset{\overline{}}{\epsilon_{\text{Rev}}}\ \)after the exercise has increased from -8.3% to – 9.5%.