code: def get_cv_error(clf,feats,scores):
      
      results={'success' : False, 'kappa' : 0, 'mae' : 0}
      try:
          cv_preds=util_functions.gen_cv_preds(clf,feats,scores)
          err=numpy.mean(numpy.abs(numpy.array(cv_preds)-scores))
          kappa=util_functions.quadratic_weighted_kappa(list(cv_preds),scores)
          results['mae']=err
          results['kappa']=kappa
          results['success']=True
      except ValueError as ex:
          # If this is hit, everything is fine.  It is hard to explain why the error occurs, but it isn't a big deal.
          msg = u"Not enough classes (0,1,etc) in each cross validation fold: {ex}".format(ex=ex)
          log.debug(msg)
      except:
          log.exception("Error getting cv error estimates.")
  
      return results
docs: Gets cross validated error for a given classifier, set of features, and scores
      clf - classifier
      feats - features to feed into the classified and cross validate over
      scores - scores associated with the features -- feature row 1 associates with score 1, etc.