Run 10461717

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.de composition._factor_analysis.FactorAnalysis,step_2=sklearn.discriminant_ana lysis.LinearDiscriminantAnalysis)(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	2
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priors	null
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage	null
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver	"svd"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariance	false
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol	0.00022038386757157927
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columns	null
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copy	false
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power	38
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter	9461
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components	5
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	"randomized"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol	0.15202009839418618
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbose	false