code: public final Frame score(Frame fr, boolean adapt) {
      if (isSupervised()) {
        int ridx = fr.find(responseName());
        if (ridx != -1) { // drop the response for scoring!
          fr = new Frame(fr);
          fr.remove(ridx);
        }
      }
      // Adapt the Frame layout - returns adapted frame and frame containing only
      // newly created vectors
      Frame[] adaptFrms = adapt ? adapt(fr,false) : null;
      // Adapted frame containing all columns - mix of original vectors from fr
      // and newly created vectors serving as adaptors
      Frame adaptFrm = adapt ? adaptFrms[0] : fr;
      // Contains only newly created vectors. The frame eases deletion of these vectors.
      Frame onlyAdaptFrm = adapt ? adaptFrms[1] : null;
      // Invoke scoring
      Frame output = scoreImpl(adaptFrm);
      // Be nice to DKV and delete vectors which i created :-)
      if (adapt) onlyAdaptFrm.delete();
      return output;
    }
docs: Bulk score the frame <code>fr</code>, producing a Frame result; the 1st Vec is the
  predicted class, the remaining Vecs are the probability distributions.
  For Regression (single-class) models, the 1st and only Vec is the
  prediction value.
  
  The flat <code>adapt</code>
  @param fr frame which should be scored
  @param adapt a flag enforcing an adaptation of <code>fr</code> to this model. If flag
  is <code>false</code> scoring code expect that <code>fr</code> is already adapted.
  @return a new frame containing a predicted values. For classification it contains a column with
  prediction and distribution for all response classes. For regression it contains only
  one column with predicted values.