class_docstr: Fit and predict one or more estimators on one or more datasets.
class_docstr_tok: ["Fit","and","predict","one","or","more","estimators","on","one","or","more","datasets","."]
docstr: Fit strategies on datasets.
docstr_tok: ["Fit","strategies","on","datasets","."]
code: def fit(self, overwrite_fitted_strategies=False, verbose=False):
      """Fit strategies on datasets."""
      for (
          task,
          dataset,
          data,
          strategy,
          cv_fold,
          train_idx,
          _test_idx,
      ) in self._iter():
          # skip strategy, if overwrite is set to False and fitted
          # strategy already exists
          if (
              not overwrite_fitted_strategies
              and self.results.check_fitted_strategy_exists(
                  strategy, data.dataset_name
              )
          ):
              log.warn(
                  f"Skipping strategy: {strategy.name} on CV-fold: "
                  f"{cv_fold} of dataset: {dataset.name}"
              )
              continue
  
          # else fit and save fitted strategy
          else:
              train = data.iloc[train_idx]
              self._print_progress(
                  dataset.name,
                  strategy.name,
                  cv_fold,
                  "train",
                  "fit",
                  verbose,
              )
              strategy.fit(task, train)
              self.results.save_fitted_strategy(
                  strategy=strategy,
                  dataset_name=data.dataset_name,
                  cv_fold=cv_fold,
              )
  
file_path: sktime/sktime/benchmarking/orchestration.py
imports: import logging
  import pandas
  from sklearn.base import clone
  from sktime.benchmarking.tasks import TSCTask, TSRTask
label_desc: Use image node_id 3 for calling the Orchestrator obj's underlying member method code with example usage: obj.fit(overwrite_fitted_strategies, verbose) without return types