Figure , shows mean SHAP values for selected variables, discriminated according to the solvent employed. As previously mentioned, the model employed to analyze the initial data set from the literature will try to adjust the individual contributions of each variable over the main outcome, i.e., particle size. SHAP values are an indication of exactly that. In other words, SHAP values show which synthetic variable contributes the most to determine the final particle size. The insets in Figure , showcase correlation between the prediction model and the input data. Although SHAP values are a great tool to understand the role of the variables on the overall response of the system, the correlation does not necessarily means causality, and thus the data set employed and its possible bias must be taken into account when analyzing the model output.