Run 10462771

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,s tep_1=sklearn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.05432002534149338
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	130
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.49318531923443365
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	981
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.10888781155576524
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.21600097056723744
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.16596602639086555
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3928542651497749
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.preprocessing._data.RobustScaler(1)_copy	false
sklearn.preprocessing._data.RobustScaler(1)_quantile_range	[34.071576249655486, 81.16048238794737]
sklearn.preprocessing._data.RobustScaler(1)_with_centering	false
sklearn.preprocessing._data.RobustScaler(1)_with_scaling	false
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier)(1)_verbose	false