class_docstr: A 'Stacking Cross-Validation' classifier for scikit-learn estimators.
  
  New in mlxtend v0.4.3
  
  Parameters
  ----------
  classifiers : array-like, shape = [n_classifiers]
      A list of classifiers.
      Invoking the `fit` method on the `StackingCVClassifer` will fit clones
      of these original classifiers that will
      be stored in the class attribute `self.clfs_` if `use_clones=True`.
  meta_classifier : object
      The meta-classifier to be fitted on the ensemble of
      classifiers
  use_probas : bool (default: False)
      If True, trains meta-classifier based on predicted probabilities
      instead of class labels.
  drop_proba_col : string (default: None)
      Drops extra "probability" column in the feature set, because it is
      redundant:
      p(y_c) = 1 - p(y_1) + p(y_2) + ... + p(y_{c-1}).
      This can be useful for meta-classifiers that are sensitive to perfectly
      collinear features.
      If 'last', drops last probability column.
      If 'first', drops first probability column.
      Only relevant if `use_probas=True`.
  cv : int, cross-validation generator or an iterable, optional (default: 2)
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
        - None, to use the default 2-fold cross validation,
        - integer, to specify the number of folds in a `(Stratified)KFold`,
        - An object to be used as a cross-validation generator.
        - An iterable yielding train, test splits.
      For integer/None inputs, it will use either a `KFold` or
      `StratifiedKFold` cross validation depending the value of `stratify`
      argument.
  shuffle : bool (default: True)
      If True,  and the `cv` argument is integer, the training data will be
      shuffled at fitting stage prior to cross-validation. If the `cv`
      argument is a specific cross validation technique, this argument is
      omitted.
  random_state : int, RandomState instance or None, optional (default: None)
      Constrols the randomness of the cv splitter. Used when `cv` is
      integer and `shuffle=True`. New in v0.16.0.
  stratify : bool (default: True)
      If True, and the `cv` argument is integer it will follow a stratified
      K-Fold cross validation technique. If the `cv` argument is a specific
      cross validation technique, this argument is omitted.
  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
                     and which fold is currently being used for fitting
      - `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-classifier will be trained both on the predictions
      of the original classifiers and the original dataset.
      If False, the meta-classifier will be trained only on the predictions
      of the original classifiers.
  store_train_meta_features : bool (default: False)
      If True, the meta-features computed from the training data used
      for fitting the meta-classifier stored in the
      `self.train_meta_features_` array, which can be
      accessed after calling `fit`.
  use_clones : bool (default: True)
      Clones the classifiers for stacking classification if True (default)
      or else uses the original ones, which will be refitted on the dataset
      upon calling the `fit` method. Hence, if use_clones=True, the original
      input classifiers will remain unmodified upon using the
      StackingCVClassifier's `fit` method.
      Setting `use_clones=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.
  n_jobs : int or None, optional (default=None)
      The number of CPUs to use to do the computation.
      `None` means 1 unless in a `joblib.parallel_backend` context.
      `-1` means using all processors.
      for more details. New in v0.16.0.
  pre_dispatch : int, or string, optional
      Controls the number of jobs that get dispatched during parallel
      execution. Reducing this number can be useful to avoid an
      explosion of memory consumption when more jobs get dispatched
      than CPUs can process. This parameter can be:
          - None, in which case all the jobs are immediately
            created and spawned. Use this for lightweight and
            fast-running jobs, to avoid delays due to on-demand
            spawning of the jobs
          - An int, giving the exact number of total jobs that are
            spawned
          - A string, giving an expression as a function of n_jobs,
            as in '2*n_jobs'
      New in v0.16.0.
  
  Attributes
  ----------
  clfs_ : list, shape=[n_classifiers]
      Fitted classifiers (clones of the original classifiers)
  meta_clf_ : estimator
      Fitted meta-classifier (clone of the original meta-estimator)
  train_meta_features : numpy array, shape = [n_samples, n_classifiers]
      meta-features for training data, where n_samples is the
      number of samples
      in training data and n_classifiers is the number of classfiers.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/classifier/StackingCVClassifier/
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code: def _stack_first_level_features(self, X, meta_features):
      if sparse.issparse(X):
          stack_fn = sparse.hstack
      else:
          stack_fn = np.hstack
  
      return stack_fn((X, meta_features))
  
imports: import numpy
  from scipy import sparse
  from sklearn.base import TransformerMixin, clone
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection._split import check_cv
  from ..externals.estimator_checks import check_is_fitted
  from ..externals.name_estimators import _name_estimators
  from ..utils.base_compostion import _BaseXComposition
  from ._base_classification import _BaseStackingClassifier
label_desc: Use image node_id 7 for calling the StackingCVClassifier obj's underlying member method code with example usage: obj._stack_first_level_features(X, meta_features) and returns: stack_fn