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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)

Visibility: public Uploaded 21-10-2020 by Mohit Aggarwal
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9 0 runs

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estimator | sklearn.linear_model._logistic.LogisticRegression(4) | 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 |

cv | Determines the cross-validation splitting strategy
Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices
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 | default: null |

error_score | 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 | default: NaN |

estimator | 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 | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}} |

iid | If True, return the average score across folds, weighted by the number of samples in each test set. In this case, 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 .. deprecated:: 0.22 Parameter ``iid`` is deprecated in 0.22 and will be removed in 0.24 | default: "deprecated" |

n_iter | Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution | default: 10 |

n_jobs | Number of jobs to run in parallel
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary | default: -1 |

param_distributions | Dictionary with parameters names (`str`) 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 uniformly If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above | default: {"C": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "scipy.stats._continuous_distns.uniform_gen", "a": 0.0, "b": 1.0, "args": [1, 100], "kwds": {}}}, "penalty": ["l1", "l2", "elasticnet"]} |

pre_dispatch | 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 str, giving an expression as a function of n_jobs, as in '2*n_jobs' | default: "2*n_jobs" |

random_state | Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions
Pass an int for reproducible output across multiple
function calls
See :term:`Glossary | default: 0 |

refit | Refit an estimator using the best found parameters on the whole dataset For multiple metric evaluation, this needs to be a `str` denoting the scorer that would be used to find the best parameters for refitting the estimator at the end Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given the ``cv_results``. In that case, the ``best_estimator_`` and ``best_params_`` will be set according to the returned ``best_index_`` while the ``best_score_`` attribute will not be available 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_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this speci... | default: true |

return_train_score | If ``False``, the ``cv_results_`` attribute will not include training scores 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 .. versionadded:: 0.19 .. versionchanged:: 0.21 Default value was changed from ``True`` to ``False`` | default: false |

scoring | A single str (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 score method is used | default: null |

verbose | Controls the verbosity: the higher, the more messages | default: 0 |

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