Run 10460356

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEs timator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=skle arn.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	0.5079700238063408
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	33
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.3337125863228991
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1653
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.10370985537730193
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.012573020678285316
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3211780173353695
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.13201944852269298
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"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate	0.0011039818735953408
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators	1155
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state	42
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose	false