Run 10459524

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.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	false
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.5901619594151318
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	113
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	479
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.7444612191015462
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.697952151824607
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.42680503419365556
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.19627068731584282
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.23046948117730498
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	547
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