docstr: Method to evaluate folds results and generate mean and standard deviation
  
  :param verbose: If True, print evaluation results
  :type verbose: bool, default True
code: def evaluate(self, verbose=True):
      """
      Method to evaluate folds results and generate mean and standard deviation
  
      :param verbose: If True, print evaluation results
      :type verbose: bool, default True
  
      """
  
      mean_dict = defaultdict(dict)
      std_dict = defaultdict(dict)
  
      for metric in self.metrics:
          mean_dict[metric.upper()] = np.mean(
              self.folds_results[metric.upper()]
          )
          std_dict[metric.upper()] = np.std(
              self.folds_results[metric.upper()]
          )
  
      if verbose:
          if self.as_table:
              header = ""
              values_mean = ""
              values_std = ""
              for metric in self.metrics:
                  header += metric.upper() + self.table_sep
                  values_mean += (
                      str(round(mean_dict[metric.upper()], 6))
                      + self.table_sep
                  )
                  values_std += (
                      str(round(std_dict[metric.upper()], 6))
                      + self.table_sep
                  )
              print("Metric%s%s" % (self.table_sep, header))
              print("Mean%s%s" % (self.table_sep, values_mean))
              print("STD%s%s" % (self.table_sep, values_std))
          else:
              evaluation_mean = "Mean:: "
              evaluation_std = "STD:: "
              for metrics in self.metrics:
                  evaluation_mean += "%s: %.6f " % (
                      metrics.upper(),
                      mean_dict[metrics.upper()],
                  )
                  evaluation_std += "%s: %.6f " % (
                      metrics.upper(),
                      std_dict[metrics.upper()],
                  )
              print(evaluation_mean)
              print(evaluation_std)
  
file_path: CaseRecommender/caserec/utils/cross_validation.py
imports: from collections import defaultdict
  import numpy
  import shutil
  from caserec.utils.split_database import SplitDatabase
label_desc: Use image node_id 4 for calling the CrossValidation obj's underlying member method code with example usage: obj.evaluate(verbose) without return types