Run 2353

Task 801 (Learning Curve) diabetes Uploaded 17-10-2024 by Continuous Integration
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TEST993e196f8csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.Simple Imputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.Var ianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV (estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)	Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
TEST993e196f8csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_memory	null
TEST993e196f8csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
TEST993e196f8csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_verbose	false
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_copy	true
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_strategy	"median"
TEST993e196f8csklearn.impute._base.SimpleImputer(1)_verbose	0
TEST993e196f8csklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid	"deprecated"
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_param_distributions	{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TEST993e196f8csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_presort	"deprecated"
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST993e196f8csklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"