Run 3397

Task 801 (Learning Curve) diabetes Uploaded 12-01-2024 by Continuous Integration
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TESTad052b0f4csklearn.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``.
TESTad052b0f4csklearn.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
TESTad052b0f4csklearn.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"}}]
TESTad052b0f4csklearn.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
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_copy	true
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_strategy	"median"
TESTad052b0f4csklearn.impute._base.SimpleImputer(1)_verbose	0
TESTad052b0f4csklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TESTad052b0f4csklearn.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]}
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TESTad052b0f4csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTad052b0f4csklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"