Run 10459098

Task 9981 (Supervised Classification) cnae-9 Uploaded 20-05-2020 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	0.6476516685393157
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	153
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.9707884467624054
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1010
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.012709791156647988
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.1446359977016164
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3370953169728455
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3281141446333447
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.svm._classes.SVC(4)_C	252.46527619271833
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	-21.56839121717107
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	5
sklearn.svm._classes.SVC(4)_gamma	0.7063720542957729
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	false
sklearn.svm._classes.SVC(4)_tol	5.203192791968656e-05
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