Run 10459137

Task 9981 (Supervised Classification) cnae-9 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	0.10819138441166931
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	176
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.29795099577908224
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	743
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.029337833831216797
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.2791845443206457
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.0003444607904009133
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.2792225188649904
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	"l2"
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