Run 10457906

Task 9900 (Supervised Classification) abalone Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Ro bustScaler,step_2=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	false
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.618896783934749
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	9
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	1637
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.2799958763442346
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.6236289886702749
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.11469771021941025
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.029348399848819817
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.06987652781663994
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	767
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	[30.39969251041995, 78.19318201511537]
sklearn.preprocessing._data.RobustScaler(1)_with_centering	false
sklearn.preprocessing._data.RobustScaler(1)_with_scaling	false
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false