Run 10463200

Task 3021 (Supervised Classification) sick Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step _1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.svm ._classes.SVC)(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.svm._classes.SVC(4)_C	9.78883482981436e-05
sklearn.svm._classes.SVC(4)_break_ties	false
sklearn.svm._classes.SVC(4)_cache_size	200
sklearn.svm._classes.SVC(4)_class_weight	null
sklearn.svm._classes.SVC(4)_coef0	174.09648302053492
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	5
sklearn.svm._classes.SVC(4)_gamma	0.017949962597778382
sklearn.svm._classes.SVC(4)_kernel	"poly"
sklearn.svm._classes.SVC(4)_max_iter	-1
sklearn.svm._classes.SVC(4)_probability	false
sklearn.svm._classes.SVC(4)_random_state	42
sklearn.svm._classes.SVC(4)_shrinking	false
sklearn.svm._classes.SVC(4)_tol	0.056129971751534835
sklearn.svm._classes.SVC(4)_verbose	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	2691
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	"lapack"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol	1.0317038123437197
sklearn.impute._base.MissingIndicator(1)_error_on_new	true
sklearn.impute._base.MissingIndicator(1)_features	"all"
sklearn.impute._base.MissingIndicator(1)_missing_values	NaN
sklearn.impute._base.MissingIndicator(1)_sparse	"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.svm._classes.SVC)(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=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.svm._classes.SVC)(1)_verbose	false