The selection of the above cutoff resulted in 8 protonated analytes being classified as forming a diagnostic addition product ion with MOP and 28 protonated analytes being considered as non-diagnostic. Since this split was unbalanced, the Cohen Kappa Statistic was used to compare the success of different models. A Kappa statistic of zero indicated that the model performs at random and a value of positive 1 indicated a perfect classifier (see Methods section for details). To further investigate the effect of this cutoff value, models created with a 70% cutoff were compared to those created with 10%, 20%, 30%, 40%, 50%, 60%, and 90% cutoffs to ensure that this choice was logical with respect to how the models performed for reactions not used to train the model. Note that a cutoff of 80% was not considered as it produced the same set of analytes that underwent the diagnostic reaction as the 70% cutoff. A potential alternative to the 70% cutoff was 40% as this represents the second-largest gap in the distribution of diagnostic product branching ratios (see Figure ). This value was approximately at the 67 th quantile of the data and resulted in a split of 13 analytes that resulted in a diagnostic addition reaction, compared to 23 analytes that did not. When considering the result of the binary classifier with both cutoffs, 70% and 40%, the model classified four analytes, TEMPO (an Noxide radical), 5,5-dimethyl-1-pyrroline N-oxide, methyl phenyl sulfoxide, and (ethenesulfinyl)benzene (a sulfoxide) (see Figure ) differently. Conversely, with both cutoffs, the model classified all sulfones, alcohols, and amines to undergo proton transfer or no reaction instead of forming a diagnostic addition product ion. The similarities and differences between the 70% and 40% cutoffs could be used to further understand how the model performs and assigns classifications.