newVector.addElement(new Option( "\tThe index of an attribute to output in the\n" + "\tresults. This attribute should identify an\n" + "\tinstance in order to know which instances are\n" + "\tin the test set of a cross validation. if 0\n" + "\tno output (default 0).", "I", 1, "-I <index>")); newVector.addElement(new Option( "\tAdd target and prediction columns to the result\n" + "\tfor each fold.", "P", 0, "-P"));
  public Enumeration listOptions() {    Vector newVector = new Vector(2);    newVector.addElement(new Option(	     "\tThe full class name of the classifier.\n"	      +"\teg: weka.classifiers.bayes.NaiveBayes", 	     "W", 1, 	     "-W <class name>"));    newVector.addElement(new Option(	     "\tThe index of the class for which IR statistics\n" +	     "\tare to be output. (default 1)",	     "C", 1, 	     "-C <index>"));    if ((m_Classifier != null) &&	(m_Classifier instanceof OptionHandler)) {      newVector.addElement(new Option(	     "",	     "", 0, "\nOptions specific to classifier "	     + m_Classifier.getClass().getName() + ":"));      Enumeration enum = ((OptionHandler)m_Classifier).listOptions();      while (enum.hasMoreElements()) {	newVector.addElement(enum.nextElement());      }    }