CART Class Prediction: Evaluate Existing Classifier

To evaluate an existing Classification And Regression Trees (CART) classifier, run the CART module with the classifier (model) file and a test data set.

Before you begin

Evaluating an existing classifier requires:

A *.cart.model file that contains the CART classifier.
Example file: all_aml_train.cart.model.
A gene expression data set against which to test the classifier:
- GCT or RES file that contains gene expression data.
  Example file: all_aml_test.gct.
- CLS file that identifies the class of each sample in the gene expression data.
  Example file: all_aml_test.cls.
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file formats

Step 1: CART

To test a previously built CART classifier, run the CART module. Specify the classifier (*.cart.model) and the test data set. The module creates a prediction results file (*.pred.odf) that assesses the accuracy of the predictor.

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CART

Step 2: View results

To view the prediction results file (*.pred.odf), use the PredictionResultsViewer module. The viewer lists each sample with its actual and predicted class.

Considerations

The PredictionResultsViewer provides an absolute error rate (incorrect cases/total cases) and an ROC error rate (fraction of true positives versus the fraction of false positives). Use the ROC error rate for comparing results across data sets.

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PredictionResultsViewer