This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.
The ROC curve plots the true positive rate (TPR), also known as power, on the y-axis. The ROC curve plots the false positive rate (FPR), also known as type 1 error, on the x-axis. The area under an ROC curve indicates whether the model is a good classifier.
The area under the curve with out-of-bag data is approximately 0.90. You can use the area under the curve to compare the accuracy of the Random Forests® Classification to another model, such as a TreeNet® Classification.