Run 10455858

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	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.7013086519736407
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	3479
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.2058047297600014
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.5483865098725085
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.19581113580299267
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.013028771943144328
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.3511400442770208
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1728
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