Run 736

Task 801 (Learning Curve) diabetes Uploaded 03-07-2024 by Continuous Integration
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TESTa73a6b1fc4sklearn.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``.
TESTa73a6b1fc4sklearn.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
TESTa73a6b1fc4sklearn.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"}}]
TESTa73a6b1fc4sklearn.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
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_copy	true
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_strategy	"median"
TESTa73a6b1fc4sklearn.impute._base.SimpleImputer(1)_verbose	0
TESTa73a6b1fc4sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TESTa73a6b1fc4sklearn.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]}
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TESTa73a6b1fc4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTa73a6b1fc4sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"