Run 10457987

Task 9981 (Supervised Classification) cnae-9 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.svm._cla sses.SVC)(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	0.7457605089343494
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	733
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.05988767526005501
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	455
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.09267918660990533
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.11654349927564985
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3220705387813168
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.14114739154575723
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.svm._classes.SVC(4)_C	1.247825878607968
sklearn.svm._classes.SVC(4)_break_ties	true
sklearn.svm._classes.SVC(4)_cache_size	200
sklearn.svm._classes.SVC(4)_class_weight	null
sklearn.svm._classes.SVC(4)_coef0	-22.616429978138296
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	5
sklearn.svm._classes.SVC(4)_gamma	0.00012668165195389572
sklearn.svm._classes.SVC(4)_kernel	"poly"
sklearn.svm._classes.SVC(4)_max_iter	-1
sklearn.svm._classes.SVC(4)_probability	false
sklearn.svm._classes.SVC(4)_random_state	42
sklearn.svm._classes.SVC(4)_shrinking	true
sklearn.svm._classes.SVC(4)_tol	0.020831590041147475
sklearn.svm._classes.SVC(4)_verbose	false
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.svm._classes.SVC)(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.svm._classes.SVC)(1)_verbose	false