Run 10459632

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,ste p_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	0.2172409643485942
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	12
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.25555520248668373
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	10038
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.13528691070989468
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.10739240272829154
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.14276315552797686
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.26693339241288805
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.Normalizer(1)_copy	false
sklearn.preprocessing._data.Normalizer(1)_norm	"l1"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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.Normalizer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false