Run 10462290

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 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	140
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priors	null
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage	0.22511799196804794
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver	"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariance	false
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol	0.00014699296759547686
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	16.708516771834525
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func	{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.chi2"}