Run 10559845

Task 59 (Supervised Classification) iris Uploaded 25-03-2021 by Pieter Gijsbers
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Flow

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertran sformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.p reprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data. StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForest Classifier))(1)	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.
sklearn.preprocessing._data.StandardScaler(6)_copy	true
sklearn.preprocessing._data.StandardScaler(6)_with_mean	true
sklearn.preprocessing._data.StandardScaler(6)_with_std	true
sklearn.preprocessing._data.MinMaxScaler(2)_copy	true
sklearn.preprocessing._data.MinMaxScaler(2)_feature_range	[0, 1]
sklearn.preprocessing._data.PowerTransformer(1)_copy	true
sklearn.preprocessing._data.PowerTransformer(1)_method	"yeo-johnson"
sklearn.preprocessing._data.PowerTransformer(1)_standardize	true
sklearn.preprocessing._data.RobustScaler(2)_copy	true
sklearn.preprocessing._data.RobustScaler(2)_quantile_range	[25.0, 75.0]
sklearn.preprocessing._data.RobustScaler(2)_with_centering	true
sklearn.preprocessing._data.RobustScaler(2)_with_scaling	true
sklearn.ensemble._forest.RandomForestClassifier(7)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(7)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(7)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(7)_max_features	"auto"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(7)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(7)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_n_estimators	5
sklearn.ensemble._forest.RandomForestClassifier(7)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(7)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(7)_random_state	28930
sklearn.ensemble._forest.RandomForestClassifier(7)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(7)_warm_start	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_cv	{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "18816", "shuffle": "true"}}}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_error_score	NaN
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_iid	"deprecated"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_n_iter	1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_n_jobs	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_param_distributions	{"randomforestclassifier__bootstrap": [true, false], "randomforestclassifier__criterion": ["gini", "entropy"], "randomforestclassifier__max_depth": [3, null], "randomforestclassifier__max_features": [1, 2, 3, 4], "randomforestclassifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "randomforestclassifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_random_state	2863
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_refit	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_return_train_score	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_scoring	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier))(1)_verbose	0
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "minmaxscaler", "step_name": "minmaxscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "powertransformer", "step_name": "powertransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "robustscaler", "step_name": "robustscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false