The conditional inference tree recursively estimated the association between risk factors from the final model and risk of arrest. First, the model selected the risk factor with the strongest association with arrest. Second, the model used a binary split on this risk factor at the cutpoint that maximized the discrepancy between the risk of arrest among the two subsamples (above and below the cutpoint). The model recursively repeated these steps with the next strongest predictor until the stop criterion, based on Bonferroni-adjusted p-values, was met to prevent overfitting. The results of the conditional inference tree are depicted in Figure 4.
