Run 10459886

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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	0.4869136881613527
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	4
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.30943559733745757
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2932
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.004322235736133906
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.22231449221301494
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.059127313254319185
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.1131696037039227
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.MinMaxScaler(1)_copy	false
sklearn.preprocessing._data.MinMaxScaler(1)_feature_range	[0, 1]
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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.MinMaxScaler,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false