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)