sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)
2020-01-21T00:04:20Z
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
public
openml==0.10.2,sklearn==0.23.2
0
18701
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.
0
1
0
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)