class_docstr: Cross Validation Result Class for a single model. A list of :obj:`cornac.experiment.Result`.
  
  Parameters
  ----------
  model_name: string, required
      The name of the recommender model.
      
  Attributes
  ----------    
  metric_mean: :obj:`OrderedDict`
      A dictionary containing the mean of results across all folds per-metric.
  
  metric_std: :obj:`OrderedDict`
      A dictionary containing the standard deviation of results across all folds per-metric.
code: def organize(self):
      headers = list(self[0].metric_avg_results.keys())
      data, index = [], []
      for f, r in enumerate(self):
          data.append([r.metric_avg_results[m] for m in headers])
          index.append("Fold %d" % f)
  
      data = np.asarray(data)
      mean, std = data.mean(axis=0), data.std(axis=0)
  
      for m, mean_val, std_val in zip(headers, mean, std):
          self.metric_mean[m] = mean_val
          self.metric_std[m] = std_val
  
      data = np.vstack([data, mean, std])
      data = [[NUM_FMT.format(v) for v in row] for row in data]
      index.extend(["Mean", "Std"])
      self.table = _table_format(
          data, headers, index, h_bars=[1, len(data) - 1]
      )
  
imports: import numpy
  from collections import OrderedDict
label_desc: Use image node_id 3 for calling the CVResult obj's underlying member method code with example usage: obj.organize() without return types