Run 10462635

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler ,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.preprocessing._data.StandardScaler(1)_copy	false
sklearn.preprocessing._data.StandardScaler(1)_with_mean	true
sklearn.preprocessing._data.StandardScaler(1)_with_std	false
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.7405242031468646
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	293
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	13
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	748
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.4034713556067476
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.14241473297811097
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.14698954783388607
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.08939182558514208
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.47581988167457595
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	735
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs	1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state	42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_start	false
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.StandardScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false