Home > Testing and Tuning Models > Testing Classification Models > Test Metrics for Classifica... > Classification Model Test a... > Classification Model Test V... > ROC
Receiver Operating Characteristics (ROC) compares predicted and actual target values in a binary classification model. For more information, see How to Use ROC.
First select the Target value; the ROC curves for that value are displayed.
Click Edit Custom Operating point to change the operating point.
The ROC graph displays a line showing ROC for each model. Points are marked on the graph indicating the values shown in the key at the bottom of the graph.
Below the graph, the ROC Summary results table supplements the information presented in the graph. You can minimize the table using the splitter line.
The Models grid in the lower pane contains summary information:
Name, the name of the model along with color of the model in the graphs
Area under the curve
Maximum Overall Accuracy Percent
Maximum Average Accuracy Percent
Custom Accuracy Percent
Model Accuracy Percent
Algorithm
Creation Date and time
Above the Models grid is the Browse Detail icon
Select a model and click the icon to see the ROC Detail Dialog, which displays statistics for probability thresholds.
By default, results for all models in the node are displayed. To change the list of models, click
to open Edit Test Result Selection.