Run 10459571

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 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	0.5240362397751582
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	0.039848214923329094
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	7855
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.05638698847832695
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.12003664272783406
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.1641123207952905
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.13606736968616173
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