Run 10459676

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectKBest,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	0.3230407107137394
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	8
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.7709226796218419
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	16402
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.0008923437805540724
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.20065252010688212
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.27819798432451875
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3776173267587944
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.feature_selection._univariate_selection.SelectKBest(1)_k	12
sklearn.feature_selection._univariate_selection.SelectKBest(1)_score_func	{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false