class sklearn.model_selection.GridSearchCV(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=’warn’)
[source]
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 User Guide.
Parameters: |
estimator : estimator object. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a 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, list/tuple, dict or None, default: None A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values. NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each. See Specifying multiple metrics for evaluation for an example. If None, the estimator’s default scorer (if available) is used. fit_params : dict, optional Parameters to pass to the fit method. Deprecated since version 0.19: 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:
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:
For integer/None inputs, if the estimator is a classifier and Refer User Guide for the various cross-validation strategies that can be used here. refit : boolean, or string, default=True Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end. The refitted estimator is made available at the Also for multiple metric evaluation, the attributes See 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, optional If Current default is | ||||||||||||||||||||||||||||||
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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 For instance the below given table
will be represented by a { '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 The key The For multi-metric evaluation, the scores for all the scorers are available in the best_estimator_ : estimator or dict 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 See best_score_ : float Mean cross-validated score of the best_estimator For multi-metric evaluation, this is present only if best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if best_index_ : int The index (of the The dict at For multi-metric evaluation, this is present only if 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 n_splits_ : int The number of cross-validation splits (folds/iterations). |
See also
ParameterGrid
sklearn.model_selection.train_test_split
sklearn.metrics.make_scorer
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
.
>>> 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(cv=None, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params=None, iid=..., n_jobs=1, param_grid=..., pre_dispatch=..., refit=..., return_train_score=..., scoring=..., verbose=...) >>> sorted(clf.cv_results_.keys()) ... ['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'...]
decision_function (X) | Call decision_function on the estimator with the best found parameters. |
fit (X[, y, groups]) | Run fit with all sets of parameters. |
get_params ([deep]) | Get parameters for this estimator. |
inverse_transform (Xt) | Call inverse_transform on the estimator with the best found params. |
predict (X) | Call predict on the estimator with the best found parameters. |
predict_log_proba (X) | Call predict_log_proba on the estimator with the best found parameters. |
predict_proba (X) | Call predict_proba on the estimator with the best found parameters. |
score (X[, y]) | Returns the score on the given data, if the estimator has been refit. |
set_params (**params) | Set the parameters of this estimator. |
transform (X) | Call transform on the estimator with the best found parameters. |
__init__(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=’warn’)
[source]
decision_function(X)
[source]
Call decision_function on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports decision_function
.
Parameters: |
X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. |
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fit(X, y=None, groups=None, **fit_params)
[source]
Run fit with all sets of parameters.
Parameters: |
X : array-like, shape = [n_samples, n_features] Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. **fit_params : dict of string -> object Parameters passed to the |
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get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: |
deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
inverse_transform(Xt)
[source]
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements inverse_transform
and refit=True
.
Parameters: |
Xt : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. |
---|
predict(X)
[source]
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. |
---|
predict_log_proba(X)
[source]
Call predict_log_proba on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports predict_log_proba
.
Parameters: |
X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. |
---|
predict_proba(X)
[source]
Call predict_proba on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports predict_proba
.
Parameters: |
X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. |
---|
score(X, y=None)
[source]
Returns the score on the given data, if the estimator has been refit.
This uses the score defined by scoring
where provided, and the best_estimator_.score
method otherwise.
Parameters: |
X : array-like, shape = [n_samples, n_features] Input data, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. |
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Returns: |
score : float |
set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: | self : |
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transform(X)
[source]
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform
and refit=True
.
Parameters: |
X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. |
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sklearn.model_selection.GridSearchCV
© 2007–2017 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html