Run 10464561

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscrimin antAnalysis)(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.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components	395
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priors	null
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage	0.4519338151414899
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver	"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariance	false
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol	0.05015614600212317
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbose	false
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile	28.13678768024938
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func	{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.chi2"}