run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None refit : bool, str, or callable, default=True Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a `str` denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. In that case, the ``best_estimator_`` and ``best_params_`` will be set according to the returned ``best_index_`` while the ``best_score_`` attribute will not be available. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. See ``scoring`` parameter to know more about multiple metric evaluation. See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` to see how to design a custom selection strategy using a callable via `refit`. .. versionchanged:: 0.20 Support for callable added. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. 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. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. verbose : int Controls the verbosity: the higher, the more messages. - >1 : the computation time for each fold and parameter candidate is displayed; - >2 : the score is also displayed; - >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation. pre_dispatch : int, or str, default='2*n_jobs' 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 str, giving an expression as a function of n_jobs, as in '2*n_jobs' error_score : 'raise' or numeric, default=np.nan 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 : bool, default=False If ``False``, the ``cv_results_`` attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. .. versionadded:: 0.19 .. versionchanged:: 0.21 Default value was changed from ``True`` to ``False`` 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_t...| +============+===========+============+=================+===+=========+ | 'poly' | -- | 2 | 0.80 |...| 2 | +------------+-----------+------------+-----------------+---+---------+ | 'poly' | -- | 3 | 0.70 |...| 4 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.1 | -- | 0.80 |...| 3 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.2 | -- | 0.93 |...| 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' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.70, 0.78], 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'split0_train_score' : [0.80, 0.92, 0.70, 0.93], 'split1_train_score' : [0.82, 0.55, 0.70, 0.87], 'mean_train_score' : [0.81, 0.74, 0.70, 0.90], 'std_train_score' : [0.01, 0.19, 0.00, 0.03], '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' : [0.01, 0.06, 0.04, 0.04], 'std_score_time' : [0.00, 0.00, 0.00, 0.01], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], } NOTE The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) 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``. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator For multi-metric evaluation, this is present only if ``refit`` is specified. This attribute is not available if ``refit`` is a function. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if ``refit`` is specified. 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_``). For multi-metric evaluation, this is present only if ``refit`` is specified. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). refit_time_ : float Seconds used for refitting the best model on the whole dataset. This is present only if ``refit`` is not False. .. versionadded:: 0.20 multimetric_ : bool Whether or not the scorers compute several metrics. classes_ : ndarray of shape (n_classes,) The classes labels. This is present only if ``refit`` is specified and the underlying estimator is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `n_features_in_` when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `feature_names_in_` when fit. .. versionadded:: 1.0 See Also -------- ParameterGrid : Generates all the combinations of a hyperparameter grid. 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. sklearn.metrics.make_scorer : Make a scorer from a performance metric or loss function. 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`. Examples -------- >>> from sklearn import svm, datasets >>> from sklearn.model_selection import GridSearchCV >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svc = svm.SVC() >>> clf = GridSearchCV(svc, parameters) >>> clf.fit(iris.data, iris.target) GridSearchCV(estimator=SVC(), param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')}) >>> sorted(clf.cv_results_.keys()) ['mean_fit_time', 'mean_score_time', 'mean_test_score',... 'param_C', 'param_kernel', 'params',... 'rank_test_score', 'split0_test_score',... 'split2_test_score', ... 'std_fit_time', 'std_score_time', 'std_test_score'] rL