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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)

Visibility: public Uploaded 07-11-2019 by Continuous Integration sklearn==0.18.2 numpy>=1.6.1 scipy>=0.9 4 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18.2
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Randomized search on hyper parameters. RandomizedSearchCV 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 search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.

Components

estimatorsklearn.ensemble.forest.RandomForestClassifier(19)A object of that type is instantiated for each grid point 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: 3
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"
estimatorA object of that type is instantiated for each grid point This 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_iterNumber of parameter settings that are sampled. n_iter trades off runtime vs quality of the solutiondefault: 10
n_jobsNumber of jobs to run in paralleldefault: 1
param_distributionsDictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions) If a list is given, it is sampled uniformlydefault: {"bootstrap": [true, false], "criterion": ["gini", "entropy"], "max_depth": [3, null], "max_features": [1, 2, 3, 4]}
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"
random_statePseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributionsdefault: 42
refitRefit the best estimator with the entire dataset If "False", it is impossible to make predictions using this RandomizedSearchCV instance after fittingdefault: true
return_train_scoreIf ``'False'``, the ``cv_results_`` attribute will not include training scores.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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