class_docstr: Analyze results of machine learning experiments.
docstr: Evaluate estimator performance.
  
  Calculates the average prediction error per estimator as well as the prediction
  error achieved by each estimator on individual datasets.
docstr_tok: ["Evaluate","estimator","performance",".","Calculates","the","average","prediction","error","per","estimator","as","well","as","the","prediction","error","achieved","by","each","estimator","on","individual","datasets","."]
code: def evaluate(self, metric, train_or_test="test", cv_fold="all"):
      """Evaluate estimator performance.
  
      Calculates the average prediction error per estimator as well as the prediction
      error achieved by each estimator on individual datasets.
      """
      # check input
      if isinstance(cv_fold, int) and cv_fold >= 0:
          cv_folds = [cv_fold]  # if single fold, make iterable
      elif cv_fold == "all":
          cv_folds = np.arange(self.results.cv.get_n_splits())
          if len(cv_folds) == 0:
              raise ValueError()
      else:
          raise ValueError(
              f"`cv_fold` must be either positive integer (>=0) or 'all', "
              f"but found: {type(cv_fold)}"
          )
  
      # load all predictions
      for cv_fold in cv_folds:
          for result in self.results.load_predictions(
              cv_fold=cv_fold, train_or_test=train_or_test
          ):
              # unwrap result object
              strategy_name = result.strategy_name
              dataset_name = result.dataset_name
              # index = result.index
              y_true = result.y_true
              y_pred = result.y_pred
              # y_proba = result.y_proba
  
              # compute metric
              mean, stderr = metric.compute(y_true, y_pred)
  
              # store results
              metric_dict = {
                  "dataset": dataset_name,
                  "strategy": strategy_name,
                  "cv_fold": cv_fold,
                  self._get_column_name(
                      metric.name, suffix="mean"
                  ): mean,
                  self._get_column_name(
                      metric.name, suffix="stderr"
                  ): stderr,
              }
              self._metric_dicts.append(metric_dict)
  
      # update metrics dataframe with computed metrics
      metrics = pd.DataFrame(self._metric_dicts)
      self._metrics = self._metrics.merge(metrics, how="outer")
  
      # aggregate results
      # aggregate over cv folds
      metrics_by_strategy_dataset = (
          self._metrics.groupby(["dataset", "strategy"], as_index=False)
          .agg("mean")
          .drop(columns="cv_fold")
      )
      self._metrics_by_strategy_dataset = (
          self._metrics_by_strategy_dataset.merge(
              metrics_by_strategy_dataset, how="outer"
          )
      )
      # aggregate over cv folds and datasets
      metrics_by_strategy_dataset_wo_ds = (
          metrics_by_strategy_dataset.drop(
              columns=["dataset"],
          )
      )
      metrics_by_strategy = metrics_by_strategy_dataset_wo_ds.groupby(
          ["strategy"], as_index=False
      ).agg("mean")
      self._metrics_by_strategy = self._metrics_by_strategy.merge(
          metrics_by_strategy, how="outer"
      )
  
      # append metric names
      self._metric_names.append(metric.name)
  
      # return aggregated results
      return self._metrics_by_strategy
  
imports: import itertools
  import numpy
  import pandas
  from scipy import stats
  from scipy.stats import ranksums, ttest_ind
  from sktime.benchmarking.base import BaseResults
  from sktime.exceptions import NotEvaluatedError
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 6 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate(metric, train_or_test, cv_fold) and returns: self