Run 10455319

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_thresh old.VarianceThreshold,step_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	0.10897634657163491
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	3
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.3272255379627777
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	4067
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.10438708838431064
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.1627842046770867
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.133196583350135
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.25409896979963725
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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.feature_selection._variance_threshold.VarianceThreshold,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.feature_selection._variance_threshold.VarianceThreshold(2)_threshold	0.0