Run 1130

Task 801 (Learning Curve) diabetes Uploaded 11-01-2024 by Continuous Integration
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TESTf7d34c49a8sklearn.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``.
TESTf7d34c49a8sklearn.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
TESTf7d34c49a8sklearn.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"}}]
TESTf7d34c49a8sklearn.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
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_copy	true
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_strategy	"median"
TESTf7d34c49a8sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTf7d34c49a8sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TESTf7d34c49a8sklearn.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]}
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TESTf7d34c49a8sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTf7d34c49a8sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"