Run 10461960

Task 3891 (Supervised Classification) gina_agnostic Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectPercentile,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	0.3624232166858158
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	83
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.396256717116788
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1341
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.03150076722344688
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.4268128465774284
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.264000443889565
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.2093631356254137
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile	16.708516771834525
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func	{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.chi2"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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._univariate_selection.SelectPercentile,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false