Weighted Voting Class Prediction: Evaluate Existing Classifier

Before you begin

Evaluating an existing classifier requires:

• A *.model.odf file that contains the Weighted Voting classifier.
  Example file: all_aml_train.preprocessed.wv.model.odf.
• 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.

  learn more: file formats

Step 1: WeightedVoting

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

learn more: WeightedVoting

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.

learn more: PredictionResultsViewer