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Table 2 Accuracy rates for random forest models using initiation of phases of exploratory behaviour (EX) or initiation of long distance oriented flight (LD) as a response and weather/environmental variables as predictors

From: Proximate cues to phases of movement in a highly dispersive waterfowl, Anas superciliosa

Response Set of variables available for each tree Area under ROC curve—training Area under ROC curve—validation % Classification Accuracy—training % Classification Accuracy—validation
Initiation of exploratory flight Bird ID 0.98 0.97 74 69
Tmax
MinPress
Humidity
Rain_Week
SolarEx
Rain_Month
NDVI
Humidity
Initiation of long distance oriented flight Bird ID 0.92 0.78 71 72
Tmin
Tmax
MinPress
Humidity
Rain_3Wks
Rain_Week
  1. The ROC (Receiver Operating Characteristic) curve is a means of measuring the performance of a binary classifier by plotting true positives against false positives, giving an indication of the sensitivity of the model. For explanation of abbreviations see Table 3 below. See Table A2 in Additional file 1 for examples of models using combinations of variables that did not produce classification accuracies high enough to warrant inclusion in the main results