![]() See GlossaryĬhanged in version v0.20: n_jobs default changed from 1 to None refit bool, str, or callable, default=True None means 1 unless in a joblib.parallel_backend context. See Specifying multiple metrics for evaluation for an example. Names and the values are the metric scores Ī dictionary with metric names as keys and callables a values. ![]() If scoring represents multiple scores, one can use:Ī callable returning a dictionary where the keys are the metric If scoring represents a single score, one can use:Ī single string (see The scoring parameter: defining model evaluation rules) Ī callable (see Defining your scoring strategy from metric functions) that returns a single value. Strategy to evaluate the performance of the cross-validated model on scoring str, callable, list, tuple or dict, default=None Parameter settings to try as values, or a list of suchĭictionaries, in which case the grids spanned by each dictionary param_grid dict or list of dictionariesĭictionary with parameters names ( str) as keys and lists of This is assumed to implement the scikit-learn estimator interface.Įither estimator needs to provide a score function, The parameters of the estimator used to apply these methods are optimizedīy cross-validated grid-search over a parameter grid. “decision_function”, “transform” and “inverse_transform” if they are It also implements “score_samples”, “predict”, “predict_proba”, GridSearchCV implements a “fit” and a “score” method. GridSearchCV ( estimator, param_grid, *, scoring = None, n_jobs = None, refit = True, cv = None, verbose = 0, pre_dispatch = '2*n_jobs', error_score = nan, return_train_score = False ) ¶Įxhaustive search over specified parameter values for an estimator. Sklearn.model_selection.GridSearchCV ¶ class sklearn.model_selection.
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