The decision boundaries in Figure show that each method is able to capture this broad trend, but the varying ability to capture the additional non-linear boundaries for these two features illustrates their broader success for the full set of features. For linear regression and linear support vectors, the decision boundary is weighted heavily toward deciding based on charge difference in order to capture the points that are active due to large charge differences despite low spin density, but purely linear methods cannot simultaneously do this and readily identify sites that are active due to sufficiently high spin density despite a smaller charge difference. In contrast, decision trees and radial basis support vectors are able to capture this nonlinearity, but they do not appear to have cleanly captured the general trend and achieve roughly the same performance. The random forest classifier (RFC) model demonstrates superior performance relative to the other models, so we elected to use RFC models for further analysis on active site classification.