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Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,s tep_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.ensemble._forest.RandomForestClassifier(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.9968326009486949
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	4
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	179
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.18473057159879572
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.44642147742692295
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.09879324916512228
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.38688898408959355
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.06496833834892979
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	464
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.preprocessing._data.RobustScaler(1)_copy	false
sklearn.preprocessing._data.RobustScaler(1)_quantile_range	[36.28071212516332, 96.52862287373077]
sklearn.preprocessing._data.RobustScaler(1)_with_centering	true
sklearn.preprocessing._data.RobustScaler(1)_with_scaling	false
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,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.RobustScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false