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Task 801 (Learning Curve) diabetes Uploaded 12-01-2024 by Continuous Integration
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TEST660c5398efsklearn.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``.
TEST660c5398efsklearn.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
TEST660c5398efsklearn.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"}}]
TEST660c5398efsklearn.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
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_copy	true
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_strategy	"median"
TEST660c5398efsklearn.impute._base.SimpleImputer(1)_verbose	0
TEST660c5398efsklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid	"deprecated"
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TEST660c5398efsklearn.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]}
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TEST660c5398efsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_presort	"deprecated"
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST660c5398efsklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"