Run 10464534

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.naive_bayes.BernoulliNB),step_1=sklearn.tree._classes.Decisio nTreeClassifier)(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	0.6345991871984084
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	9
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.5698629689167783
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	7369
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.030405657826457568
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.33254495523953603
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.190855008373214
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.25457227305733915
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.BernoulliNB(11)_alpha	19.302215539848206
sklearn.naive_bayes.BernoulliNB(11)_binarize	0.0
sklearn.naive_bayes.BernoulliNB(11)_class_prior	null
sklearn.naive_bayes.BernoulliNB(11)_fit_prior	false
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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.naive_bayes.BernoulliNB),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false