KNN Class Prediction: Classify Unknown Sample

Before you begin

Classifying an unknown sample requires:

• A *.knn.model file that contains the KNN classifier.
  Example file: all_aml_train.preprocessed.knn.model.odf.
• A gene expression data set containing the unknown samples:
  • 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 as 'unknown'.
    Example file: all_aml_test_unknown.cls.

  learn more: file formats

Step 1: KNN

To classify unknown samples using a previously built KNN classifier, run the KNN module. Use the model file parameter to specify the classifier (*.knn.model) and the test filename and test class filename parameters to specify the data set containing the unknown samples. The module creates a prediction results file (*.pred.odf) that lists each sample with its actual class ('unknown') and its predicted class.

learn more: KNN

Step 2: View results

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

Considerations

• Ignore the actual class, which was unknown.
• Ignore the error rates, which are designed to evaluate the class predictor against known data.

learn more: PredictionResultsViewer