protected boolean[] canHandleOnlyClass(boolean nominalPredictor,boolean numericPredictor,boolean stringPredictor,boolean datePredictor,boolean relationalPredictor,int classType){
  print("only class in data");
  printAttributeSummary(nominalPredictor,numericPredictor,stringPredictor,datePredictor,relationalPredictor,false,classType);
  print("...");
  ArrayList<String> accepts=new ArrayList<String>();
  accepts.add("class");
  accepts.add("zeror");
  int numTrain=getNumInstances(), numTest=getNumInstances(), missingLevel=0;
  boolean predictorMissing=false, classMissing=false;
  return runBasicTest(false,false,false,false,false,false,classType,missingLevel,predictorMissing,classMissing,numTrain,numTest,2,accepts);
}

protected boolean [ ] canHandleOnlyClass ( boolean nominalPredictor , boolean numericPredictor , boolean stringPredictor , boolean datePredictor , boolean relationalPredictor , int classType ) { print ( "only class in data" ) ; printAttributeSummary ( nominalPredictor , numericPredictor , stringPredictor , datePredictor , relationalPredictor , false , classType ) ; print ( "..." ) ; ArrayList < String > accepts = new ArrayList < String > ( ) ; accepts . add ( "class" ) ; accepts . add ( "zeror" ) ; int numTrain = getNumInstances ( ) , numTest = getNumInstances ( ) , missingLevel = 0 ; boolean predictorMissing = false , classMissing = false ; return runBasicTest ( false , false , false , false , false , false , classType , missingLevel , predictorMissing , classMissing , numTrain , numTest , 2 , accepts ) ; }
checks whether the scheme can handle data that contains only the class attribute . if a scheme cannot build a proper model with that data , it should default back to a zeror model .
