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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))

sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))

Visibility: public Uploaded 07-11-2019 by Continuous Integration sklearn==0.18.2 numpy>=1.6.1 scipy>=0.9 5 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18.2
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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.

Components

estimatorsklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(8)This is assumed to implement the scikit-learn estimator interface Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed

Parameters

cvDetermines 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 ` for the various cross-validation strategies that can be used heredefault: null
error_scoreValue 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 errordefault: "raise"
estimatorThis is assumed to implement the scikit-learn estimator interface Either estimator needs to provide a ``score`` function, or ``scoring`` must be passeddefault: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_paramsParameters to pass to the fit method
iidIf 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 foldsdefault: true
n_jobsNumber of jobs to run in paralleldefault: 1
param_gridDictionary 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 settingsdefault: {"classifier__max_depth": [1, 2, 3, 4, 5], "imputer__strategy": ["mean", "median"]}
pre_dispatchControls 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'default: "2*n_jobs"
refitRefit the best estimator with the entire dataset If "False", it is impossible to make predictions using this GridSearchCV instance after fittingdefault: true
return_train_scoreIf ``'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()) ... ...default: true
scoringA 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 useddefault: null
verboseControls the verbosity: the higher, the more messagesdefault: 0

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