Run 10453340

Task 9946 (Supervised Classification) wdbc Uploaded 18-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.tree._classes.DecisionTreeClassifi er)(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	0.5432668764968459
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	13
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.4278151020315579
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	542
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.003303606894307665
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.22945403332486616
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.013516213117403916
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.12088079676515079
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.pipeline.Pipeline(step_0=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false