@Override public String toString(){
  StringBuffer text=new StringBuffer();
  if (m_trainInstances == null) {
    text.append("\tOneR feature evaluator has not been built yet");
  }
 else {
    text.append("\tOneR feature evaluator.\n\n");
    text.append("\tUsing ");
    if (m_evalUsingTrainingData) {
      text.append("training data for evaluation of attributes.");
    }
 else {
      text.append("" + getFolds() + " fold cross validation for evaluating "+ "attributes.");
    }
    text.append("\n\tMinimum bucket size for OneR: " + getMinimumBucketSize());
  }
  text.append("\n");
  return text.toString();
}

@ Override public String toString ( ) { StringBuffer text = new StringBuffer ( ) ; if ( m_trainInstances == null ) { text . append ( "\tOneR feature evaluator has not been built yet" ) ; } else { text . append ( "\tOneR feature evaluator.\n\n" ) ; text . append ( "\tUsing " ) ; if ( m_evalUsingTrainingData ) { text . append ( "training data for evaluation of attributes." ) ; } else { text . append ( "" + getFolds ( ) + " fold cross validation for evaluating " + "attributes." ) ; } text . append ( "\n\tMinimum bucket size for OneR: " + getMinimumBucketSize ( ) ) ; } text . append ( "\n" ) ; return text . toString ( ) ; }
return a description of the evaluator
