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Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.Fac torAnalysis,step_1=sklearn.naive_bayes.BernoulliNB)(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.naive_bayes.BernoulliNB(11)_alpha	104.71563436474398
sklearn.naive_bayes.BernoulliNB(11)_binarize	0.0
sklearn.naive_bayes.BernoulliNB(11)_class_prior	null
sklearn.naive_bayes.BernoulliNB(11)_fit_prior	true
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	8103
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components	2
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	0.50694004688802
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.naive_bayes.BernoulliNB)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.naive_bayes.BernoulliNB)(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.naive_bayes.BernoulliNB)(1)_verbose	false