class_docstr: Abstract base class for hyper parameter search with cross-validation.
class_docstr_tok: ["Abstract","base","class","for","hyper","parameter","search","with","cross-validation","."]
docstr: Call predict on the estimator with the best found parameters.
  
  Only available if ``refit=True`` and the underlying estimator supports
  ``predict``.
  
  Parameters
  ----------
  X : indexable, length n_samples
      Must fulfill the input assumptions of the
      underlying estimator.
  
  Returns
  -------
  y_pred : ndarray of shape (n_samples,)
      The predicted labels or values for `X` based on the estimator with
      the best found parameters.
docstr_tok: ["Call","predict","on","the","estimator","with","the","best","found","parameters",".","Only","available","if","``","refit=True","``","and","the","underlying","estimator","supports","``","predict","``",".","Parameters","--","--","--","--","--","X",":","indexable",",","length","n_samples","Must","fulfill","the","input","assumptions","of","the","underlying","estimator",".","Returns","--","--","--","-","y_pred",":","ndarray","of","shape","(","n_samples",",",")","The","predicted","labels","or","values","for","`","X","`","based","on","the","estimator","with","the","best","found","parameters","."]
code: def predict(self, X):
      """Call predict on the estimator with the best found parameters.
  
      Only available if ``refit=True`` and the underlying estimator supports
      ``predict``.
  
      Parameters
      ----------
      X : indexable, length n_samples
          Must fulfill the input assumptions of the
          underlying estimator.
  
      Returns
      -------
      y_pred : ndarray of shape (n_samples,)
          The predicted labels or values for `X` based on the estimator with
          the best found parameters.
      """
      check_is_fitted(self)
      return self.best_estimator_.predict(X)
  
imports: import numbers
  import operator
  import time
  import warnings
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict
  from collections.abc import Iterable, Mapping, Sequence
  from functools import partial, reduce
  from itertools import product
  import numpy
  from numpy.ma import MaskedArray
  from scipy.stats import rankdata
  from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
  from ..exceptions import NotFittedError
  from ..metrics import check_scoring
  from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer, get_scorer_names
  from ..utils import Bunch, check_random_state
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
  from ..utils.metaestimators import available_if
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_method_params, check_is_fitted, indexable
  from ._split import check_cv
  from ._validation import _aggregate_score_dicts, _fit_and_score, _insert_error_scores, _normalize_score_results, _warn_or_raise_about_fit_failures
label_desc: Use image node_id 6 for calling the BaseSearchCV obj's underlying member method code with example usage: obj.predict(X) and returns: self