Run 10458814

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.naive_ba yes.MultinomialNB)(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.naive_bayes.MultinomialNB(6)_alpha	0.8526517738009419
sklearn.naive_bayes.MultinomialNB(6)_class_prior	null
sklearn.naive_bayes.MultinomialNB(6)_fit_prior	false
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.naive_bayes.MultinomialNB)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.naive_bayes.MultinomialNB)(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.naive_bayes.MultinomialNB)(1)_verbose	false