class_docstr: Abstract class for confusion matrix
  
  You shouldn't instantiate this class.
  You might instantiate ConfusionMatrix or BinaryConfusionMatrix classes
class_docstr_tok: ["Abstract","class","for","confusion","matrix","You","should","n't","instantiate","this","class",".","You","might","instantiate","ConfusionMatrix","or","BinaryConfusionMatrix","classes"]
docstr: Returns an OrderedDict with overall statistics
docstr_tok: ["Returns","an","OrderedDict","with","overall","statistics"]
code: def stats_overall(self):
      """
      Returns an OrderedDict with overall statistics
      """
      df = self._df_confusion
      d_stats = collections.OrderedDict()
  
      d_class_agreement = class_agreement(df)
  
      key = "Accuracy"
      try:
          d_stats[key] = d_class_agreement["diag"]  # 0.35
      except KeyError:
          d_stats[key] = np.nan
  
      key = "95% CI"
      try:
          d_stats[key] = binom_interval(
              np.sum(np.diag(df)), df.sum().sum()
          )  # (0.1539, 0.5922)
      except:  # noqa
          d_stats[key] = np.nan
  
      d_prop_test = prop_test(df)
      d_stats["No Information Rate"] = "ToDo"  # 0.8
      d_stats["P-Value [Acc > NIR]"] = d_prop_test["p.value"]  # 1
      d_stats["Kappa"] = d_class_agreement["kappa"]  # 0.078
      d_stats["Mcnemar's Test P-Value"] = "ToDo"  # np.nan
  
      return d_stats
  
imports: import numpy
  import pandas
  import collections
  import pandas_ml
  from pandas_ml.confusion_matrix.stats import binom_interval, class_agreement, prop_test
label_desc: Use image node_id 21 for calling the ConfusionMatrixAbstract obj's underlying member method code with example usage: obj.stats_overall() and returns: d_stats