Run 9185

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.t ree.tree.DecisionTreeClassifier))(4)	Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV 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 grid-search over a parameter grid.
sklearn.tree.tree.DecisionTreeClassifier(8)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(8)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(8)_max_depth	1
sklearn.tree.tree.DecisionTreeClassifier(8)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(8)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_decrease	0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_min_impurity_split	null
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(8)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(8)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(8)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(8)_random_state	10609
sklearn.tree.tree.DecisionTreeClassifier(8)_splitter	"best"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_cv	null
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_error_score	"raise"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_fit_params	null
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_iid	true
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_n_jobs	1
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_param_grid	{"classifier__max_depth": [1, 2, 3, 4, 5], "imputer__strategy": ["mean", "median"]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_refit	true
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_return_train_score	"warn"
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_scoring	null
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier))(4)_verbose	0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(4)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier)(4)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.preprocessing.imputation.Imputer(4)_axis	0
sklearn.preprocessing.imputation.Imputer(4)_copy	true
sklearn.preprocessing.imputation.Imputer(4)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(4)_strategy	"mean"
sklearn.preprocessing.imputation.Imputer(4)_verbose	0