Run 10459854

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeat ures,step_1=sklearn.tree._classes.DecisionTreeClassifier)(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	0.2509023396551636
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	20
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.022171435593975695
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	11467
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.04366380622953972
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.4020952991652422
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.4548145070219585
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.2562961487754871
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.preprocessing._data.PolynomialFeatures(1)_degree	2
sklearn.preprocessing._data.PolynomialFeatures(1)_include_bias	true
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_only	false
sklearn.preprocessing._data.PolynomialFeatures(1)_order	"F"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,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=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false