Fig. 3: Comparison in results derived from the random forest model with hyperparameters tuned and untuned. | npj Climate and Atmospheric Science

Fig. 3: Comparison in results derived from the random forest model with hyperparameters tuned and untuned.

From: An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Fig. 3

a Boxplots of statistics derived from the tuned RF model (tu) and untuned RF model (ut). b Boxplots of the trends for PM2.5 observation (\({{\rm{PM}}}_{2.5}^{{\rm{OBS}}}\)), emission-related PM2.5 (\({{\rm{PM}}}_{2.5}^{{\rm{EMI}}}\)), and meteorology-related PM2.5 (\({{\rm{PM}}}_{2.5}^{{\rm{MET}}}\)); c Scatterplot between emission-related trends of PM2.5 derived from RF and CMAQ models. NS. and *** represent the differences between the two groups that are not significant (p > 0.05) and are significant at 0.001 levels.

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