Run 6610

Task 801 (Learning Curve) diabetes Uploaded 12-11-2024 by Continuous Integration
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TESTb752ebead6sklearn.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``.
TESTb752ebead6sklearn.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
TESTb752ebead6sklearn.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"}}]
TESTb752ebead6sklearn.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
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_copy	true
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_strategy	"median"
TESTb752ebead6sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTb752ebead6sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid	"deprecated"
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TESTb752ebead6sklearn.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]}
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TESTb752ebead6sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_presort	"deprecated"
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTb752ebead6sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"