Common use of Random Forest Clause in Contracts

Random Forest. The literature review performed before this pilot project identified a method that used Random Forest classification on LIDAR metrics to predict the presence of snags in various size classes (Martinuzzi, et al., 2009). This method was attempted to see how results compared to the presence/absence logistic regression model used above, with the results presented in Table 14 and Table 15. The size classes identified by the researchers in that paper were tested, but only two of those classes are reported here, the ≥ 6” class (15cm) and the ≥ 10” class (25cm). The ≥ 6” class, is similar to the 5” minimum diameter cutoff used in our model. The ≥ 10” class is reported because it has approximately equal numbers of plots with and without snags. For small size classes, nearly all plots had snags, while for large size classes, almost no plots had snags, making the presence/absence classes unbalanced. Random Forest tends to work better with balanced presence/absence classes. It is expected that a model predicting all absence or all presence, would be very accurate, but have limited utility, in that it only provides information that is already known. The ≥ 10” class provides the most realistic and useful model results.

Random Forest. The literature review performed before this pilot project identified a method that used Random Forest classification on LIDAR metrics to predict the presence of snags in various size classes (MartinuzziXxxxxxxxxx, et al., 2009). This method was attempted to see how results compared to the presence/absence logistic regression model used above, with the results presented in Table 14 and Table 15. The size classes identified by the researchers in that paper were tested, but only two of those classes are reported here, the ≥ 6” class (15cm) and the ≥ 10” class (25cm). The ≥ 6” class, is similar to the 5” minimum diameter cutoff used in our model. The ≥ 10” class is reported because it has approximately equal numbers of plots with and without snags. For small size classes, nearly all plots had snags, while for large size classes, almost no plots had snags, making the presence/absence classes unbalanced. Random Forest tends to work better with balanced presence/absence classes. It is expected that a model predicting all absence or all presence, would be very accurate, but have limited utility, in that it only provides information that is already known. The ≥ 10” class provides the most realistic and useful model results.