| cv | 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 | default: 3 |
| error_score | 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 | default: NaN |
| estimator | This is assumed to implement the scikit-learn estimator interface
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}} |
| n_jobs | Number of jobs to 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 | default: null |
| param_grid | Dictionary with parameters names (`str`) 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 | default: {"base_estimator__C": [0.01, 0.1, 10], "base_estimator__gamma": [0.01, 0.1, 10]} |
| pre_dispatch | 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' | default: "2*n_jobs" |
| refit | 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
s... | default: true |
| return_train_score | 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``
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'... | default: false |
| scoring | Strategy to evaluate the performance of the cross-validated model on
the test set
If `scoring` represents a single score, one can use:
- a single string (see :ref:`scoring_parameter`);
- a callable (see :ref:`scoring`) that returns a single value
If `scoring` represents multiple scores, one can use:
- a list or tuple of unique strings;
- a callable returning a dictionary where the keys are the metric
names and the values are the metric scores;
- a dictionary with metric names as keys and callables a values
See :ref:`multimetric_grid_search` for an example | default: null |
| verbose | Controls the verbosity: the higher, the more messages | default: 0 |