 To determine how well the model generalized to unseen data, they divided the data randomly 100 times into a training set and a testing set, fit the model parameters on each training set, and scored its predictions on the unseen testing set. The average percent of correct predictions on unseen data was 92%. All components of the model except number of the recipient NP made a statistically significant difference—almost all at the 0.001 level.