Run 10455927

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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	true
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrap	false
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.43508787066364907
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	4
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	3920
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.24817731934549297
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.5350540898888332
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.36010938554560323
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.06763512230418096
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.38718592639600513
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1118
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