Run 10455496

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble ._weight_boosting.AdaBoostClassifier)(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``.
sklearn.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.7813263961695894
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.8931869713637344
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2862
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.03318395581759475
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.022080062889239135
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.48795676412149686
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3977129823848426
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate	0.0010315249414753294
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators	1458
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state	42
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose	false