/** * Builds the classifier. * * @param data the data to train with * @throws Exception if classifier can't be built successfully */ @Override public void buildClassifier(Instances data) throws Exception { reset(); m_header = new Instances(data, 0); if (m_selectedSplitMetric == GINI_SPLIT) { m_splitMetric = new GiniSplitMetric(); } else { m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain); } data = new Instances(data); data.deleteWithMissingClass(); for (int i = 0; i < data.numInstances(); i++) { updateClassifier(data.instance(i)); } // can classifier handle the data? getCapabilities().testWithFail(data); }
/** * Builds the classifier. * * @param data the data to train with * @throws Exception if classifier can't be built successfully */ @Override public void buildClassifier(Instances data) throws Exception { reset(); m_header = new Instances(data, 0); if (m_selectedSplitMetric == GINI_SPLIT) { m_splitMetric = new GiniSplitMetric(); } else { m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain); } data = new Instances(data); data.deleteWithMissingClass(); for (int i = 0; i < data.numInstances(); i++) { updateClassifier(data.instance(i)); } // can classifier handle the data? getCapabilities().testWithFail(data); }