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sklearn：sklearn.GridSearchCV函数的简介、使用方法之详细攻略

發(fā)布時間：2025/3/21 编程问答 30 豆豆

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sklearn：sklearn.GridSearchCVl函數(shù)的簡介、使用方法之詳細攻略

sklearn.GridSearchCV函數(shù)的簡介

1、參數(shù)說明

2、功能代碼

sklearn.GridSearchCV函數(shù)的使用方法

sklearn.GridSearchCV函數(shù)的簡介

1、參數(shù)說明

? """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 :ref:`User Guide <grid_search>`.	窮舉搜索指定參數(shù)值的估計量。重要的成員是要被訓練的、預測的。 GridSearchCV實現(xiàn)了一個“fit”和一個“score”方法。 ? ? 如果在使用的估計器中實現(xiàn)了“predict”、“predict_proba”、“decision_function”、“transform”和“inverse_transform”，那么它還實現(xiàn)了“predict”、“predict_proba”、“decision_function”、“transform”和“inverse_transform”。應用這些方法的估計器的參數(shù)是通過參數(shù)網(wǎng)格上交叉驗證的網(wǎng)格搜索來優(yōu)化的。 ?
??Parameters ????---------- ????estimator : estimator object. ????This is assumed to implement the scikit-learn estimator interface. ????Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. ???? ????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 :ref:`scoring_parameter`) or a callable ? ?(see :ref:`scoring`) 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 :ref:`multimetric_grid_search` 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:: 0.19????``fit_params`` as a constructor argument was deprecated in version????0.19 and will be removed in version 0.21. Pass fit parameters to????the ``fit`` method instead.	參數(shù) ????---------- estimator: estimator對象。這里假設實現(xiàn)了sci -learn estimator接口。要么估計器需要提供一個' ' score ' '函數(shù)，要么' ' scores ' '必須被傳遞。 ???? param_grid:字典的字典或列表以參數(shù)名稱(字符串)作為鍵的Dictionary和嘗試作為值的參數(shù)設置列表，或此類Dictionary的列表，在這種情況下，將探索列表中每個Dictionary跨越的網(wǎng)格。這允許搜索任何序列的參數(shù)設置。 ? scoring :string, callable, list/tuple, dict or None, default: None一個字符串(參見:ref: ' scoring_parameter ')或callable(參見:ref: ' scores ')來評估測試集上的預測。對于評估多個指標，要么給出一個(惟一的)字符串列表，要么給出一個以名稱為鍵、以可調(diào)用項為值的dict。注意，當使用自定義記分員時，每個記分員應該返回一個值。返回值列表/數(shù)組的度量函數(shù)可以包裝成多個評分器，每個評分器返回一個值。參見:ref: ' multimetric_grid_search '獲取示例。如果沒有，則使用估計器的默認記分員(如果可用)。 ? fit_params: dict，可選參數(shù)傳遞給fit方法。. .作為構(gòu)造函數(shù)參數(shù)的 deprecated:: 0.19 ' ' fit_params ' '在0.19版本中被棄用，將在0.21版本中刪除。而是將fit參數(shù)傳遞給' ' fit ' '方法。
? ??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: ????- 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 '2n_jobs' ???? ????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: ????- 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 <cross_validation>` for the various cross-validation strategies that can be used here.	n_jobs: int，默認值為1 要并行運行的作業(yè)數(shù)。 pre_dispatch: int或string可選控制在并行執(zhí)行期間分派的作業(yè)的數(shù)量。當分配的作業(yè)比cpu處理的多時，減少這個數(shù)量可以避免內(nèi)存消耗的激增。這個參數(shù)可以是: -沒有，在這種情況下，所有的工作是立即創(chuàng)造和產(chǎn)生。將其用于輕量級和快速運行的作業(yè)，以避免由于按需生成作業(yè)而導致的延遲 -一個整數(shù)，給出確切的總數(shù)的工作，是產(chǎn)生 -一個字符串，給出一個表達式作為n_jobs的函數(shù)，如'2n_jobs' iid*:布爾值，默認=真如果為真，則假定數(shù)據(jù)在所有折痕上是同分布的，損失最小的是每個樣本的總損失，而不是折痕上的平均損失。 cv: int，交叉驗證生成器或可迭代的，可選的確定交叉驗證分割策略。 cv的可能輸入有: - None，使用默認的3倍交叉驗證， - integer，用于指定“(分層)KFold”中的折疊數(shù)， -用作交叉驗證生成器的對象。 -一個迭代的屈服序列，測試分裂。對于整數(shù)/無輸入，如果估計器是一個分類器，而' ' y ' '是二進制或多類的，則使用:class: ' hierarchfiedkfold '。在所有其他情況下，使用:class: ' KFold '。參考:ref: ' User Guide <cross_validation> '，了解這里可以使用的各種交叉驗證策略。</cross_validation>
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 ``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_parameters_`` 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. ???? ????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.	refit: boolean，或string, default=True 使用在整個數(shù)據(jù)集上找到的最佳參數(shù)來重新編譯估計器。對于多個度量評估，這需要是一個表示記分員的字符串，用于在最后找到重新編譯估計器的最佳參數(shù)。修改后的估計器在' ' best_estimator_ ' '屬性中可用，并且允許在這個' ' GridSearchCV ' '實例中直接使用' ' predict ' '。 ? 同樣，對于多個度量求值，屬性' ' best_index_ ' '、' ' best_score_ ' '和' ' best_parameters_ ' '只有在' ' refit ' '被設置并全部被確定為w.r時才可用。t這個特定的得分手。參見“評分”參數(shù)以了解更多關(guān)于多重度量評估的信息。 verbose :整數(shù) 控制冗余:越高，消息越多。 error_score: 'raise'(默認)或數(shù)值如果在估計器擬合中出現(xiàn)錯誤，則將值賦給該分數(shù)。如果設置為“引發(fā)”，則會引發(fā)錯誤。如果給定一個數(shù)值，則會引發(fā)FitFailedWarning。此參數(shù)不影響refit步驟，因為后者總是會引起錯誤。
??return_train_score : boolean, optional ????If ``False``, the ``cv_results_`` attribute will not include training scores. ???? ????Current default is ``'warn'``, which behaves as ``True`` in addition to raising a warning when a training score is looked up. ????That default will be changed to ``False`` in 0.21. ????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.	return_train_score:布爾值，可選如果' ' False ' '， ' ' cv_results_ ' '屬性將不包括訓練分數(shù)。當前的默認值是' 'warn' ' ' '，它的行為為' ' True ' '，除了在查詢訓練分數(shù)時發(fā)出警告外。默認值將在0.21中更改為' ' False ' '。計算訓練分數(shù)是用來了解不同的參數(shù)設置如何影響過擬合/欠擬合權(quán)衡。然而，計算訓練集上的分數(shù)在計算上是很昂貴的，并且并不嚴格要求選擇產(chǎn)生最佳泛化性能的參數(shù)。
? ? 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 ? ? \| ? ? ? ?0.8 ? ? ?\|...\| ? ?2 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'poly' ? ?\| ? ? -- ? ?\| ? ? ?3 ? ? \| ? ? ? ?0.7 ? ? ?\|...\| ? ?4 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'rbf' ? ? \| ? ? 0.1 ? \| ? ? -- ? ? \| ? ? ? ?0.8 ? ? ?\|...\| ? ?3 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'rbf' ? ? \| ? ? 0.2 ? \| ? ? -- ? ? \| ? ? ? ?0.9 ? ? ?\|...\| ? ?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' ?: [0.8, 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}, ...], ? ? }	屬性 cv_results_: numpy(掩蔽)ndarrays的字典以鍵作為列標頭，以值作為列的dict，可以是這樣導入到一個pandas ' ' DataFrame ' '。例如下面給出的表 ?
????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 (``'_<scorer_name>'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.)	請注意鍵“”params“”用于存儲所有參數(shù)候選項的參數(shù)設置字典列表。 ' mean_fit_time ' '、' std_fit_time ' '、' mean_score_time ' '和' std_score_time ' '都是以秒為單位的。對于多指標評估，所有評分者的分數(shù)都可以在鍵上以該評分者的名字結(jié)尾的' ' cv_results_ ' ' dict中找到(' " _<scorer_name>' ' ')，而不是上面顯示的' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' cv_results_ ' ' ' dict中找到。</scorer_name>(“split0_test_precision”,“mean_train_precision”等等)。
? ? 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 ``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. ? ?? ? ? 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.	best_estimator_: estimator或dict 由搜索選擇的估計量，即在被遺漏的數(shù)據(jù)上給出最高分(或指定最小損失)的估計量。如果' ' refit=False ' '，則不可用。有關(guān)允許值的更多信息，請參見' ' refit ' '參數(shù)。 best_score_:float best_estimator的交叉驗證平均得分對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。 best_params_: dict類型參數(shù)設置，給出了最好的結(jié)果，對舉行的數(shù)據(jù)。對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。 best_index_: int 對應最佳候選參數(shù)設置的索引(' ' cv_results_ ' '數(shù)組的索引)。 dict at ' ' search.cv_results_['params'][搜索。best_index_] ' '給出了最佳模型的參數(shù)設置，并給出了最高的平均分數(shù)(' ' search.best_score_ ' ')。對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。
? ??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). ? ?? ? ? 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`. ? ?? ? ? See Also ? ? --------- ? ? :class:`ParameterGrid`: ? ? generates all the combinations of a hyperparameter grid. ? ? :func:`sklearn.model_selection.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. ? ? :func:`sklearn.metrics.make_scorer`: ? ? Make a scorer from a performance metric or loss function.	scorer_ : f功能還是字典記分員函數(shù)用于對保留的數(shù)據(jù)進行篩選，為模型選擇最佳參數(shù)。對于多度量的評估，此屬性保存已驗證的“評分”dict，該dict將評分鍵映射到可調(diào)用的評分者。 n_splits_: int 交叉驗證分割(折疊/迭代)的數(shù)量。筆記------ 所選的參數(shù)是那些使未輸入數(shù)據(jù)的得分最大化的參數(shù)，除非傳遞了一個顯式的分數(shù)(在這種情況下使用該分數(shù))。如果將' n_jobs '設置為大于1的值，則將為網(wǎng)格中的每個點復制數(shù)據(jù)(而不是' n_jobs '時間)。如果單個作業(yè)花費的時間很少，那么這樣做是出于效率的考慮，但是如果數(shù)據(jù)集很大且沒有足夠的可用內(nèi)存，則可能會引起錯誤。在這種情況下，一個解決方案是設置' pre_dispatch '。然后，內(nèi)存多次只復制' pre_dispatch '。' pre_dispatch '的合理值是' 2 * n_jobs '。另請參閱?--------- 類:“ParameterGrid”: 生成超參數(shù)網(wǎng)格的所有組合。 :func:“sklearn.model_selection.train_test_split”: 實用工具函數(shù)，用于將數(shù)據(jù)分割成可用于擬合GridSearchCV實例的開發(fā)集和用于最終評估的評估集。 :func:“sklearn.metrics.make_scorer”: 從性能指標或損失函數(shù)中創(chuàng)建一個記分員。

2、功能代碼

class GridSearchCV Found at: sklearn.model_selection._searchclass GridSearchCV(BaseSearchCV):"""Exhaustive search over specified parameter values for an estimator."""def __init__(self, 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"):super(GridSearchCV, self).__init__(estimator=estimator, scoring=scoring, fit_params=fit_params, n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch, error_score=error_score, return_train_score=return_train_score)self.param_grid = param_grid_check_param_grid(param_grid)def _get_param_iterator(self):"""Return ParameterGrid instance for the given param_grid"""return ParameterGrid(self.param_grid)

sklearn.GridSearchCV函數(shù)的使用方法

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)... # doctest: +NORMALIZE_WHITESPACE +ELLIPSISGridSearchCV(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())... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS['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'...]

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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. ? ?? ? ? Read more in the :ref:`User Guide <grid_search>`.	窮舉搜索指定參數(shù)值的估計量。重要的成員是要被訓練的、預測的。 GridSearchCV實現(xiàn)了一個“fit”和一個“score”方法。 ? ? 如果在使用的估計器中實現(xiàn)了“predict”、“predict_proba”、“decision_function”、“transform”和“inverse_transform”，那么它還實現(xiàn)了“predict”、“predict_proba”、“decision_function”、“transform”和“inverse_transform”。應用這些方法的估計器的參數(shù)是通過參數(shù)網(wǎng)格上交叉驗證的網(wǎng)格搜索來優(yōu)化的。 ?
??Parameters ????---------- ????estimator : estimator object. ????This is assumed to implement the scikit-learn estimator interface. ????Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. ???? ????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 :ref:`scoring_parameter`) or a callable ? ?(see :ref:`scoring`) 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 :ref:`multimetric_grid_search` 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:: 0.19????``fit_params`` as a constructor argument was deprecated in version????0.19 and will be removed in version 0.21. Pass fit parameters to????the ``fit`` method instead.	參數(shù) ????---------- estimator: estimator對象。這里假設實現(xiàn)了sci -learn estimator接口。要么估計器需要提供一個' ' score ' '函數(shù)，要么' ' scores ' '必須被傳遞。 ???? param_grid:字典的字典或列表以參數(shù)名稱(字符串)作為鍵的Dictionary和嘗試作為值的參數(shù)設置列表，或此類Dictionary的列表，在這種情況下，將探索列表中每個Dictionary跨越的網(wǎng)格。這允許搜索任何序列的參數(shù)設置。 ? scoring :string, callable, list/tuple, dict or None, default: None一個字符串(參見:ref: ' scoring_parameter ')或callable(參見:ref: ' scores ')來評估測試集上的預測。對于評估多個指標，要么給出一個(惟一的)字符串列表，要么給出一個以名稱為鍵、以可調(diào)用項為值的dict。注意，當使用自定義記分員時，每個記分員應該返回一個值。返回值列表/數(shù)組的度量函數(shù)可以包裝成多個評分器，每個評分器返回一個值。參見:ref: ' multimetric_grid_search '獲取示例。如果沒有，則使用估計器的默認記分員(如果可用)。 ? fit_params: dict，可選參數(shù)傳遞給fit方法。. .作為構(gòu)造函數(shù)參數(shù)的 deprecated:: 0.19 ' ' fit_params ' '在0.19版本中被棄用，將在0.21版本中刪除。而是將fit參數(shù)傳遞給' ' fit ' '方法。
? ??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: ????- 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 '2n_jobs' ???? ????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: ????- 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 <cross_validation>` for the various cross-validation strategies that can be used here.	n_jobs: int，默認值為1 要并行運行的作業(yè)數(shù)。 pre_dispatch: int或string可選控制在并行執(zhí)行期間分派的作業(yè)的數(shù)量。當分配的作業(yè)比cpu處理的多時，減少這個數(shù)量可以避免內(nèi)存消耗的激增。這個參數(shù)可以是: -沒有，在這種情況下，所有的工作是立即創(chuàng)造和產(chǎn)生。將其用于輕量級和快速運行的作業(yè)，以避免由于按需生成作業(yè)而導致的延遲 -一個整數(shù)，給出確切的總數(shù)的工作，是產(chǎn)生 -一個字符串，給出一個表達式作為n_jobs的函數(shù)，如'2n_jobs' iid*:布爾值，默認=真如果為真，則假定數(shù)據(jù)在所有折痕上是同分布的，損失最小的是每個樣本的總損失，而不是折痕上的平均損失。 cv: int，交叉驗證生成器或可迭代的，可選的確定交叉驗證分割策略。 cv的可能輸入有: - None，使用默認的3倍交叉驗證， - integer，用于指定“(分層)KFold”中的折疊數(shù)， -用作交叉驗證生成器的對象。 -一個迭代的屈服序列，測試分裂。對于整數(shù)/無輸入，如果估計器是一個分類器，而' ' y ' '是二進制或多類的，則使用:class: ' hierarchfiedkfold '。在所有其他情況下，使用:class: ' KFold '。參考:ref: ' User Guide <cross_validation> '，了解這里可以使用的各種交叉驗證策略。</cross_validation>
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 ``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_parameters_`` 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. ???? ????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.	refit: boolean，或string, default=True 使用在整個數(shù)據(jù)集上找到的最佳參數(shù)來重新編譯估計器。對于多個度量評估，這需要是一個表示記分員的字符串，用于在最后找到重新編譯估計器的最佳參數(shù)。修改后的估計器在' ' best_estimator_ ' '屬性中可用，并且允許在這個' ' GridSearchCV ' '實例中直接使用' ' predict ' '。 ? 同樣，對于多個度量求值，屬性' ' best_index_ ' '、' ' best_score_ ' '和' ' best_parameters_ ' '只有在' ' refit ' '被設置并全部被確定為w.r時才可用。t這個特定的得分手。參見“評分”參數(shù)以了解更多關(guān)于多重度量評估的信息。 verbose :整數(shù) 控制冗余:越高，消息越多。 error_score: 'raise'(默認)或數(shù)值如果在估計器擬合中出現(xiàn)錯誤，則將值賦給該分數(shù)。如果設置為“引發(fā)”，則會引發(fā)錯誤。如果給定一個數(shù)值，則會引發(fā)FitFailedWarning。此參數(shù)不影響refit步驟，因為后者總是會引起錯誤。
??return_train_score : boolean, optional ????If ``False``, the ``cv_results_`` attribute will not include training scores. ???? ????Current default is ``'warn'``, which behaves as ``True`` in addition to raising a warning when a training score is looked up. ????That default will be changed to ``False`` in 0.21. ????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.	return_train_score:布爾值，可選如果' ' False ' '， ' ' cv_results_ ' '屬性將不包括訓練分數(shù)。當前的默認值是' 'warn' ' ' '，它的行為為' ' True ' '，除了在查詢訓練分數(shù)時發(fā)出警告外。默認值將在0.21中更改為' ' False ' '。計算訓練分數(shù)是用來了解不同的參數(shù)設置如何影響過擬合/欠擬合權(quán)衡。然而，計算訓練集上的分數(shù)在計算上是很昂貴的，并且并不嚴格要求選擇產(chǎn)生最佳泛化性能的參數(shù)。
? ? 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 ? ? \| ? ? ? ?0.8 ? ? ?\|...\| ? ?2 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'poly' ? ?\| ? ? -- ? ?\| ? ? ?3 ? ? \| ? ? ? ?0.7 ? ? ?\|...\| ? ?4 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'rbf' ? ? \| ? ? 0.1 ? \| ? ? -- ? ? \| ? ? ? ?0.8 ? ? ?\|...\| ? ?3 ? ?\| ? ? +------------+-----------+------------+-----------------+---+---------+ ? ? \| ?'rbf' ? ? \| ? ? 0.2 ? \| ? ? -- ? ? \| ? ? ? ?0.9 ? ? ?\|...\| ? ?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' ?: [0.8, 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}, ...], ? ? }	屬性 cv_results_: numpy(掩蔽)ndarrays的字典以鍵作為列標頭，以值作為列的dict，可以是這樣導入到一個pandas ' ' DataFrame ' '。例如下面給出的表 ?
????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 (``'_<scorer_name>'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.)	請注意鍵“”params“”用于存儲所有參數(shù)候選項的參數(shù)設置字典列表。 ' mean_fit_time ' '、' std_fit_time ' '、' mean_score_time ' '和' std_score_time ' '都是以秒為單位的。對于多指標評估，所有評分者的分數(shù)都可以在鍵上以該評分者的名字結(jié)尾的' ' cv_results_ ' ' dict中找到(' " _<scorer_name>' ' ')，而不是上面顯示的' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' cv_results_ ' ' ' dict中找到。</scorer_name>(“split0_test_precision”,“mean_train_precision”等等)。
? ? 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 ``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. ? ?? ? ? 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.	best_estimator_: estimator或dict 由搜索選擇的估計量，即在被遺漏的數(shù)據(jù)上給出最高分(或指定最小損失)的估計量。如果' ' refit=False ' '，則不可用。有關(guān)允許值的更多信息，請參見' ' refit ' '參數(shù)。 best_score_:float best_estimator的交叉驗證平均得分對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。 best_params_: dict類型參數(shù)設置，給出了最好的結(jié)果，對舉行的數(shù)據(jù)。對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。 best_index_: int 對應最佳候選參數(shù)設置的索引(' ' cv_results_ ' '數(shù)組的索引)。 dict at ' ' search.cv_results_['params'][搜索。best_index_] ' '給出了最佳模型的參數(shù)設置，并給出了最高的平均分數(shù)(' ' search.best_score_ ' ')。對于多度量評估，只有在指定“refit”時才會出現(xiàn)這種情況。
? ??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). ? ?? ? ? 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`. ? ?? ? ? See Also ? ? --------- ? ? :class:`ParameterGrid`: ? ? generates all the combinations of a hyperparameter grid. ? ? :func:`sklearn.model_selection.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. ? ? :func:`sklearn.metrics.make_scorer`: ? ? Make a scorer from a performance metric or loss function.	scorer_ : f功能還是字典記分員函數(shù)用于對保留的數(shù)據(jù)進行篩選，為模型選擇最佳參數(shù)。對于多度量的評估，此屬性保存已驗證的“評分”dict，該dict將評分鍵映射到可調(diào)用的評分者。 n_splits_: int 交叉驗證分割(折疊/迭代)的數(shù)量。筆記------ 所選的參數(shù)是那些使未輸入數(shù)據(jù)的得分最大化的參數(shù)，除非傳遞了一個顯式的分數(shù)(在這種情況下使用該分數(shù))。如果將' n_jobs '設置為大于1的值，則將為網(wǎng)格中的每個點復制數(shù)據(jù)(而不是' n_jobs '時間)。如果單個作業(yè)花費的時間很少，那么這樣做是出于效率的考慮，但是如果數(shù)據(jù)集很大且沒有足夠的可用內(nèi)存，則可能會引起錯誤。在這種情況下，一個解決方案是設置' pre_dispatch '。然后，內(nèi)存多次只復制' pre_dispatch '。' pre_dispatch '的合理值是' 2 * n_jobs '。另請參閱?--------- 類:“ParameterGrid”: 生成超參數(shù)網(wǎng)格的所有組合。 :func:“sklearn.model_selection.train_test_split”: 實用工具函數(shù)，用于將數(shù)據(jù)分割成可用于擬合GridSearchCV實例的開發(fā)集和用于最終評估的評估集。 :func:“sklearn.metrics.make_scorer”: 從性能指標或損失函數(shù)中創(chuàng)建一個記分員。