Run 10463731

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._pca.PCA,step_1=skle arn.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	0.2897626208782098
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	0.15419205401558894
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2151
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.1070569139706799
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.10355520534545205
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3540080497173866
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.4275303127791156
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.decomposition._pca.PCA(1)_copy	false
sklearn.decomposition._pca.PCA(1)_iterated_power	"auto"
sklearn.decomposition._pca.PCA(1)_n_components	6
sklearn.decomposition._pca.PCA(1)_random_state	42
sklearn.decomposition._pca.PCA(1)_svd_solver	"auto"
sklearn.decomposition._pca.PCA(1)_tol	2.8510085387060213
sklearn.decomposition._pca.PCA(1)_whiten	true
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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._pca.PCA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false