Run 10464659

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_1=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``.
sklearn.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.7651923435822191
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	0.13470344516250052
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	11815
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.16278849558321404
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.4630113998796718
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3883956508197724
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.14976999136472108
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	10.663728212608708
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators	336
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state	42
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false