Run 10462940

Task 3021 (Supervised Classification) sick Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step _1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.tree._clas ses.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	0.3358554243109735
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	51
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.5407923069670231
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2879
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.9258612264058352
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.38335027653739306
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.055429259405328654
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.19862252647084083
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
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	"C"
sklearn.impute._base.MissingIndicator(1)_error_on_new	true
sklearn.impute._base.MissingIndicator(1)_features	"all"
sklearn.impute._base.MissingIndicator(1)_missing_values	NaN
sklearn.impute._base.MissingIndicator(1)_sparse	"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false