So far, the Random Forest approach has been useful to understand which variables could have a significant impact on the final particles' size, although with limitations due to the clustering of the data reported. It is possible now to analyze the reported data and obtain a predicted trend on how each one of the determined significant variables might influence final particle size, while the remaining are kept constant, as summarized in Figure . This strategy is known as Partial Dependence Plot 82 and exhibits the marginal effect that one variable has on the predicted outcome from the model while accounting for the average effect of other variables.