class_docstr: Exhaustive search over specified parameter values for an estimator.
  
  Important members are fit, predict.
  
  GridSearchCV implements a "fit" and a "score" method.
  It also implements "predict", "predict_proba", "decision_function",
  "transform" and "inverse_transform" if they are implemented in the
  estimator used.
  
  The parameters of the estimator used to apply these methods are optimized
  by cross-validated grid-search over a parameter grid.
  
  Read more in the :ref:`User Guide <grid_search>`.
  
  Parameters
  ----------
  estimator : estimator object.
      This is assumed to implement the scikit-learn estimator interface.
      Either estimator needs to provide a ``score`` function,
      or ``scoring`` must be passed.
  
  param_grid : dict or list of dictionaries
      Dictionary with parameters names (string) as keys and lists of
      parameter settings to try as values, or a list of such
      dictionaries, in which case the grids spanned by each dictionary
      in the list are explored. This enables searching over any sequence
      of parameter settings.
  
  scoring : string, callable or None, default=None
      A string (see model evaluation documentation) or
      a scorer callable object / function with signature
      ``scorer(estimator, X, y)``.
      If ``None``, the ``score`` method of the estimator is used.
  
  fit_params : dict, optional
      Parameters to pass to the fit method.
  
  n_jobs : int, default=1
      Number of jobs to run in parallel.
  
  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'
  
  iid : boolean, default=True
      If True, the data is assumed to be identically distributed across
      the folds, and the loss minimized is the total loss per sample,
      and not the mean loss across the folds.
  
  cv : int, cross-validation generator or an iterable, optional
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
        - None, to use the default 3-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, if the estimator is a classifier and ``y`` is
      either binary or multiclass, :class:`StratifiedKFold` is used. In all
      other cases, :class:`KFold` is used.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
  refit : boolean, default=True
      Refit the best estimator with the entire dataset.
      If "False", it is impossible to make predictions using
      this GridSearchCV instance after fitting.
  
  verbose : integer
      Controls the verbosity: the higher, the more messages.
  
  error_score : 'raise' (default) or numeric
      Value to assign to the score if an error occurs in estimator fitting.
      If set to 'raise', the error is raised. If a numeric value is given,
      FitFailedWarning is raised. This parameter does not affect the refit
      step, which will always raise the error.
  
  return_train_score : boolean, default=True
      If ``'False'``, the ``cv_results_`` attribute will not include training
      scores.
  
  
  Examples
  --------
  >>> from sklearn import svm, datasets
  >>> from sklearn.model_selection import GridSearchCV
  >>> iris = datasets.load_iris()
  >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
  >>> svr = svm.SVC()
  >>> clf = GridSearchCV(svr, parameters)
  >>> clf.fit(iris.data, iris.target)
  ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
  GridSearchCV(cv=None, error_score=...,
         estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
                       decision_function_shape=None, degree=..., gamma=...,
                       kernel='rbf', max_iter=-1, probability=False,
                       random_state=None, shrinking=True, tol=...,
                       verbose=False),
         fit_params={}, iid=..., n_jobs=1,
         param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
         scoring=..., verbose=...)
  >>> sorted(clf.cv_results_.keys())
  ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
  ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
   'mean_train_score', 'param_C', 'param_kernel', 'params',...
   'rank_test_score', 'split0_test_score',...
   'split0_train_score', 'split1_test_score', 'split1_train_score',...
   'split2_test_score', 'split2_train_score',...
   'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]
  
  Attributes
  ----------
  cv_results_ : dict of numpy (masked) ndarrays
      A dict with keys as column headers and values as columns, that can be
      imported into a pandas ``DataFrame``.
  
      For instance the below given table
  
      +------------+-----------+------------+-----------------+---+---------+
      |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_....|
      +============+===========+============+=================+===+=========+
      |  'poly'    |     --    |      2     |        0.8      |...|    2    |
      +------------+-----------+------------+-----------------+---+---------+
      |  'poly'    |     --    |      3     |        0.7      |...|    4    |
      +------------+-----------+------------+-----------------+---+---------+
      |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |
      +------------+-----------+------------+-----------------+---+---------+
      |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |
      +------------+-----------+------------+-----------------+---+---------+
  
      will be represented by a ``cv_results_`` dict of::
  
          {
          'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                                       mask = [False False False False]...)
          'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                                      mask = [ True  True False False]...),
          'param_degree': masked_array(data = [2.0 3.0 -- --],
                                       mask = [False False  True  True]...),
          'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
          'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
          'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
          'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
          'rank_test_score'    : [2, 4, 3, 1],
          'split0_train_score' : [0.8, 0.9, 0.7],
          'split1_train_score' : [0.82, 0.5, 0.7],
          'mean_train_score'   : [0.81, 0.7, 0.7],
          'std_train_score'    : [0.03, 0.03, 0.04],
          'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
          'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
          'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
          'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
          'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
          }
  
      NOTE that the key ``'params'`` is used to store a list of parameter
      settings dict for all the parameter candidates.
  
      The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
      ``std_score_time`` are all in seconds.
  
  best_estimator_ : estimator
      Estimator that was chosen by the search, i.e. estimator
      which gave highest score (or smallest loss if specified)
      on the left out data. Not available if refit=False.
  
  best_score_ : float
      Score of best_estimator on the left out data.
  
  best_params_ : dict
      Parameter setting that gave the best results on the hold out data.
  
  best_index_ : int
      The index (of the ``cv_results_`` arrays) which corresponds to the best
      candidate parameter setting.
  
      The dict at ``search.cv_results_['params'][search.best_index_]`` gives
      the parameter setting for the best model, that gives the highest
      mean score (``search.best_score_``).
  
  scorer_ : function
      Scorer function used on the held out data to choose the best
      parameters for the model.
  
  n_splits_ : int
      The number of cross-validation splits (folds/iterations).
  
  Notes
  ------
  The parameters selected are those that maximize the score of the left out
  data, unless an explicit score is passed in which case it is used instead.
  
  If `n_jobs` was set to a value higher than one, the data is copied for each
  point in the grid (and not `n_jobs` times). This is done for efficiency
  reasons if individual jobs take very little time, but may raise errors if
  the dataset is large and not enough memory is available.  A workaround in
  this case is to set `pre_dispatch`. Then, the memory is copied only
  `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
  n_jobs`.
  
  See Also
  ---------
  :class:`ParameterGrid`:
      generates all the combinations of a hyperparameter grid.
  
  :func:`sklearn.model_selection.train_test_split`:
      utility function to split the data into a development set usable
      for fitting a GridSearchCV instance and an evaluation set for
      its final evaluation.
  
  :func:`sklearn.metrics.make_scorer`:
      Make a scorer from a performance metric or loss function.
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code: def __init__(
      self,
      estimator,
      param_grid,
      scoring=None,
      fit_params=None,
      n_jobs=1,
      iid=True,
      refit=True,
      cv=None,
      verbose=0,
      pre_dispatch="2*n_jobs",
      error_score="raise",
      return_train_score=True,
  ):
      super(GridSearchCV, self).__init__(
          estimator=estimator,
          scoring=scoring,
          fit_params=fit_params,
          n_jobs=n_jobs,
          iid=iid,
          refit=refit,
          cv=cv,
          verbose=verbose,
          pre_dispatch=pre_dispatch,
          error_score=error_score,
          return_train_score=return_train_score,
      )
      self.param_grid = param_grid
      _check_param_grid(param_grid)
  
imports: from __future__ import print_function
  from __future__ import division
  from abc import ABCMeta, abstractmethod
  from collections import Mapping, namedtuple, Sized, defaultdict, Sequence
  from functools import partial, reduce
  from itertools import product
  import operator
  import warnings
  import numpy
  from ..base import BaseEstimator, is_classifier, clone
  from ..base import MetaEstimatorMixin
  from ._split import check_cv
  from ._validation import _fit_and_score
  from ..exceptions import NotFittedError
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..utils import check_random_state
  from ..utils.fixes import sp_version
  from ..utils.fixes import rankdata
  from ..utils.fixes import MaskedArray
  from ..utils.random import sample_without_replacement
  from ..utils.validation import indexable, check_is_fitted
  from ..utils.metaestimators import if_delegate_has_method
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 1 to create a new GridSearchCV object from inherited base classes: BaseSearchCV with example: obj = GridSearchCV(estimator, param_grid, scoring, fit_params, n_jobs, iid, refit, cv, verbose, pre_dispatch, error_score, return_train_score)