Run 10464438

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,ste p_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	0.5838239326500594
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	4
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.11740334802419572
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	10599
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.06496521670228343
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.2272748908278294
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3715126256961941
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3027701774183982
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.decomposition._fastica.FastICA(1)_algorithm	"parallel"
sklearn.decomposition._fastica.FastICA(1)_fun	"exp"
sklearn.decomposition._fastica.FastICA(1)_fun_args	null
sklearn.decomposition._fastica.FastICA(1)_max_iter	855
sklearn.decomposition._fastica.FastICA(1)_n_components	7
sklearn.decomposition._fastica.FastICA(1)_random_state	42
sklearn.decomposition._fastica.FastICA(1)_tol	0.7028868744742722
sklearn.decomposition._fastica.FastICA(1)_w_init	null
sklearn.decomposition._fastica.FastICA(1)_whiten	true
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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.decomposition._fastica.FastICA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false