Visibility: public Uploaded 25-11-2022 by Continuous Integration sklearn==0.19.2 numpy>=1.8.2 scipy>=0.13.3 1 runs
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  • openml-python python scikit-learn sklearn sklearn_0.19.2
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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.


estimatorTEST2e8dfded61sklearn.ensemble.forest.RandomForestClassifier(1)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


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: {"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "null", "shuffle": "true"}}}
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 .. 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 insteaddefault: null
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: 5
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], "min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
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 distributions If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`default: null
refitRefit an estimator using the best found parameters on the whole dataset For multiple metric evaluation, this needs to be a string denoting the scorer that would be 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 ``RandomizedSearchCV`` 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 evaluationdefault: true
return_train_scoreIf ``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.default: "warn"
scoringA 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 useddefault: null
verboseControls the verbosity: the higher, the more messagesdefault: 0


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