Run 10464462

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._factor_analysis.Fac torAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.3818411044970613
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	6
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	13918
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.9374939021132603
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.6163499133865081
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.3270188624172011
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.4630027926585986
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.2097055119256373
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1711
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs	1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state	42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_start	false
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copy	false
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power	3
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter	5671
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components	7
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_noise_variance_init	null
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_random_state	42
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_svd_method	"lapack"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol	3.037336863053271
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false