The Machine Learning Evaluate Classifier tool evaluates a classifier using labeled rasters that may or may not have been used during training. It generates a report containing statistics about the classifiers performance against the input rasters, and provides a confusion matrix of all classes as part of the report.

You can also write a script to evaluate a classifier using the MachineLearningEvaluateClassifier task.

1. From the Toolbox, select Machine Learning > Machine Learning Evaluate Classifier. The Machine Learning Evaluate Classifier dialog appears.
2. Click the Browse button next to the Input Raster field. In the Data Selection dialog that appears, one or more Machine Learning labeled rasters, then click OK.
3. In the Input Model field, select a trained machine learning model to evaluate with the input rasters.

4. In the Output Report field, enter a location and filename for the report.

5. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow and select Save Parameter Values, then specify the location and filename to save to. Note that some parameter types, such as rasters, vectors, and ROIs, will not be saved with the file. To apply the saved task settings, click the down arrow and select Restore Parameter Values, then select the file where you previously stored your settings.

6. You can run the process in the background on your computer or in the background on an ENVI Server (if available). Click the down arrow next to the OK button and select one of the following:

   • Run Task in the Background: Runs the process on your computer. This method does not use an ENVI Server. The job progress will show in the ENVI Job Console's Background Jobs tab.
   • Run Task on envi_server_name. Runs the process on the selected ENVI Server. The job progress will show in the ENVI Job Console's envi_server_name tab.

   In ENVI Help, see ENVI Job Console for details on monitoring ENVI Server jobs and viewing results, and see ENVI Servers for details on setting up an ENVI Server.

7. Click OK.

See Also

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Machine Learning Labeling Tool