Run 9236

Task 801 (Learning Curve) diabetes Uploaded 25-11-2022 by Continuous Integration
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TESTe3f1f9584asklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imput ation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshol d.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSea rchCV(estimator=sklearn.tree.tree.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 to None.
TESTe3f1f9584asklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memory	null
TESTe3f1f9584asklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.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"}}]
TESTe3f1f9584asklearn.preprocessing.imputation.Imputer(1)_axis	0
TESTe3f1f9584asklearn.preprocessing.imputation.Imputer(1)_copy	true
TESTe3f1f9584asklearn.preprocessing.imputation.Imputer(1)_missing_values	"NaN"
TESTe3f1f9584asklearn.preprocessing.imputation.Imputer(1)_strategy	"median"
TESTe3f1f9584asklearn.preprocessing.imputation.Imputer(1)_verbose	0
TESTe3f1f9584asklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv	3
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score	"raise"
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_fit_params	null
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iid	true
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter	10
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobs	1
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_param_distributions	{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state	33003
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refit	true
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_score	"warn"
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoring	null
TESTe3f1f9584asklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose	0
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_class_weight	null
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_criterion	"gini"
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_max_depth	null
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_max_features	null
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_split	null
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split	2
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_presort	false
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_random_state	62501
TESTe3f1f9584asklearn.tree.tree.DecisionTreeClassifier(1)_splitter	"best"