Run 10464280

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBin sDiscretizer,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	0.9383592225182917
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	32
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.43315841702149693
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	8545
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.13237345776528794
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.048691659579770355
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.32217961674758955
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.1569517963225734
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_encode	"onehot"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins	24
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy	"uniform"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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._discretization.KBinsDiscretizer,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false