Run 10463490

Task 3021 (Supervised Classification) sick Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step _1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._weight _boosting.AdaBoostClassifier)(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._weight_boosting.AdaBoostClassifier(2)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate	1.1446588537302897e-06
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators	1415
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state	42
sklearn.preprocessing._data.RobustScaler(1)_copy	false
sklearn.preprocessing._data.RobustScaler(1)_quantile_range	[44.33337409530728, 63.71587801109393]
sklearn.preprocessing._data.RobustScaler(1)_with_centering	true
sklearn.preprocessing._data.RobustScaler(1)_with_scaling	false
sklearn.impute._base.MissingIndicator(1)_error_on_new	true
sklearn.impute._base.MissingIndicator(1)_features	"all"
sklearn.impute._base.MissingIndicator(1)_missing_values	NaN
sklearn.impute._base.MissingIndicator(1)_sparse	"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose	false