Examples of weka.classifiers.Classifier.buildClassifier()

weka.classifiers.Classifier.buildClassifier()
Generates a classifier. Must initialize all fields of the classifier that are not being set via options (ie. multiple calls of buildClassifier must always lead to the same result). Must not change the dataset in any way. @param data set of instances serving as training data @exception Exception if the classifier has not been generated successfully

    }


    private void validationWinner(Instances randData,Instances validShuff, Classifier currentClassifier) throws Exception{
        Evaluation eval_validationE = new Evaluation(randData);
        Classifier eval_clas = Classifier.makeCopy(currentClassifier);
        eval_clas.buildClassifier(randData);
        eval_validationE.evaluateModel(eval_clas, validShuff);


        PhaseIwrep.res_pctCorrect_valid = eval_validationE.pctCorrect();
        PhaseIwrep.res_ROC_valid = eval_validationE.weightedAreaUnderROC();

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            insample_ACC[0] = loggy_eval.pctCorrect();
            insample_ROC[0] = loggy_eval.weightedAreaUnderROC();
        
        Evaluation loggy_eval_validation = new Evaluation(learn);
        Classifier loggy_valid = Classifier.makeCopy(loggy_class);
        loggy_valid.buildClassifier(learn);
        loggy_eval_validation.evaluateModel(loggy_valid, hold);
        validation_ACC[0] = loggy_eval_validation.pctCorrect();
        validation_ROC[0] = loggy_eval_validation.weightedAreaUnderROC();
        
        // DECISION TABLE

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            insample_ACC[1] = dtable_eval.pctCorrect();
            insample_ROC[1] = dtable_eval.weightedAreaUnderROC();
            
        Evaluation dtable_eval_validation = new Evaluation(learn);
        Classifier dtable_valid = Classifier.makeCopy(dtable_class);
        dtable_valid.buildClassifier(learn);
        dtable_eval_validation.evaluateModel(dtable_valid, hold);
        validation_ACC[1] = dtable_eval_validation.pctCorrect();
        validation_ROC[1] = dtable_eval_validation.weightedAreaUnderROC();
        
        }

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            insample_ACC[0] = eval.pctCorrect();
            insample_ROC[0] = eval.weightedAreaUnderROC();
        
        Evaluation eval_validation = new Evaluation(learn);
        Classifier valid_loggy = Classifier.makeCopy(linReg);
        valid_loggy.buildClassifier(learn);
        eval_validation.evaluateModel(valid_loggy, hold);
            validation_ACC[0] = eval.pctCorrect();
            validation_ROC[0] = eval.weightedAreaUnderROC();        
        }
        }

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    public void testGetPredictedLabels() {
        System.out.println("getPredictedLabels");


        Classifier classifier = new J48();
        try {
            classifier.buildClassifier(instances);
        } catch (Exception e) {
            assertTrue("Classifier could not be trained", false);
        }


        WekaModel instance = new WekaModel(classifier);

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            }
        }


        try {
            System.out.print("Training SMO on " + examples.size() + " examples... ");
            classifier.buildClassifier(examples.getInstances());
            System.out.println("done.");


            WekaModel model = new WekaModel(classifier);
            return model;
        } catch (Exception ex) {

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        Classifier classifier = log;


        try {
            System.out.print("Training on " + examples.size() + " examples... ");
            classifier.buildClassifier(examples.getInstances());
            System.out.println("done.");


            WekaModel model = new WekaModel(classifier);
            return model;
        } catch (Exception ex) {

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