Receiver operating characteristic (ROC) curve for Random Forests® Classification

Note

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.

Interpretation

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 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.

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