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Task 801 (Learning Curve) diabetes Uploaded 11-01-2024 by Continuous Integration
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TEST68b7c86572sklearn.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``.
TEST68b7c86572sklearn.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
TEST68b7c86572sklearn.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"}}]
TEST68b7c86572sklearn.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
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_copy	true
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_fill_value	null
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_strategy	"median"
TEST68b7c86572sklearn.impute._base.SimpleImputer(1)_verbose	0
TEST68b7c86572sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TEST68b7c86572sklearn.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]}
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TEST68b7c86572sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_split	null
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TEST68b7c86572sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"