evaluation.crossValidateModel(classifier, dataSet.getData(), this.numFolds, new Random(1)); result.append("evaluation summary:"); result.append("\n");
/** * Perform crossvalidation * * @param clf Classifier * @param data Full dataset * @throws Exception */ public static void crossValidate(Classifier clf, Instances data) throws Exception { Evaluation ev = new Evaluation(data); ev.crossValidateModel(clf, data, 10, new Random(42)); logger.info(ev.toSummaryString()); }
public void getLinearCombination(List<OWLClassExpression> descriptions){ //get common data Instances data = buildData(descriptions); //compute linear regression model data.setClassIndex(data.numAttributes() - 1); AbstractClassifier model = new LinearRegression(); model = new J48(); try { model.buildClassifier(data); // System.out.println(model); // AddExpression filter = new AddExpression(); // filter.setExpression("a1^2"); // FilteredClassifier filteredClassifier = new FilteredClassifier(); // filteredClassifier.setClassifier(model); // filteredClassifier.setFilter(filter); // filteredClassifier.buildClassifier(data); // logger.debug(filteredClassifier.getClassifier()); Evaluation eval = new Evaluation(data); eval.crossValidateModel(model, data, 10, new Random(1)); System.out.println(eval.toSummaryString(true)); } catch (Exception e) { e.printStackTrace(); } }
eval.crossValidateModel(aClassifier, data, numFolds, new Random(curRun)); long elapsedTime = System.currentTimeMillis() - millis;
eval.crossValidateModel(aClassifier, data, numFolds, new Random(curRun)); long elapsedTime = System.currentTimeMillis() - millis;
eval.crossValidateModel(this.classifier, trainingSet, 10, new Random(1));
if (m_Test == null) { if (m_Folds >= 2) { eval.crossValidateModel(classifier, m_Train, m_Folds, new Random(m_Owner.getSeed()));
o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random( m_randomSeed));
o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random( m_randomSeed));
m_Evaluation.crossValidateModel(m_BaseClassifier, trainCopy, m_folds, Rnd);
m_Evaluation.crossValidateModel(m_BaseClassifier, trainCopy, m_folds, Rnd);