class_docstr: A Stacking regressor for scikit-learn estimators for regression.
  
  Parameters
  ----------
  regressors : array-like, shape = [n_regressors]
      A list of regressors.
      Invoking the `fit` method on the `StackingRegressor` will fit clones
      of those original regressors that will
      be stored in the class attribute
      `self.regr_`.
  
  meta_regressor : object
      The meta-regressor to be fitted on the ensemble of
      regressors
  
  verbose : int, optional (default=0)
      Controls the verbosity of the building process.
      - `verbose=0` (default): Prints nothing
      - `verbose=1`: Prints the number & name of the regressor being fitted
      - `verbose=2`: Prints info about the parameters of the
                     regressor being fitted
      - `verbose>2`: Changes `verbose` param of the underlying regressor to
         self.verbose - 2
  
  use_features_in_secondary : bool (default: False)
      If True, the meta-regressor will be trained both on
      the predictions of the original regressors and the
      original dataset.
      If False, the meta-regressor will be trained only on
      the predictions of the original regressors.
  
  store_train_meta_features : bool (default: False)
      If True, the meta-features computed from the training data
      used for fitting the
      meta-regressor stored in the `self.train_meta_features_` array,
      which can be
      accessed after calling `fit`.
  
  
  Attributes
  ----------
  regr_ : list, shape=[n_regressors]
      Fitted regressors (clones of the original regressors)
  
  meta_regr_ : estimator
      Fitted meta-regressor (clone of the original meta-estimator)
  
  coef_ : array-like, shape = [n_features]
      Model coefficients of the fitted meta-estimator
  
  intercept_ : float
      Intercept of the fitted meta-estimator
  
  train_meta_features : numpy array,
      shape = [n_samples, len(self.regressors)]
      meta-features for training data, where n_samples is the
      number of samples
      in training data and len(self.regressors) is the number of regressors.
  
  refit : bool (default: True)
      Clones the regressors for stacking regression if True (default)
      or else uses the original ones, which will be refitted on the dataset
      upon calling the `fit` method. Setting refit=False is
      recommended if you are working with estimators that are supporting
      the scikit-learn fit/predict API interface but are not compatible
      to scikit-learn's `clone` function.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/regressor/StackingRegressor/
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code: def named_regressors(self):
      return _name_estimators(self.regressors)
  
file_path: mlxtend/mlxtend/regressor/stacking_regression.py
imports: import numpy
  import scipy.sparse
  from sklearn.base import RegressorMixin, TransformerMixin, clone
  from sklearn.utils import check_X_y
  from ..externals.estimator_checks import check_is_fitted
  from ..externals.name_estimators import _name_estimators
  from ..utils.base_compostion import _BaseXComposition
label_desc: Use image node_id 2 for calling the StackingRegressor obj's underlying member method code with example usage: obj.named_regressors() and returns: _name_estimators