Receiver operating characteristic (ROC) curve for Fit Model and Discover Key Predictors with TreeNet® Classification


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.


For classification trees, the area under the ROC curve values typically range from 0.5 to 1. Larger values indicate a better classification model. When the model can perfectly separate the classes, then the area under the curve is 1. When the model cannot separate the classes better than a random assignment, then the area under the curve is 0.5. The red dotted line indicates the random assignment case.

The area under the test curve is approximately 0.91. Compare the training results and the test results to see whether there are overfitting problems with the model for the training data set.