Run 10454777

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.Fac torAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.7382590781841806
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	951
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.37691281171761737
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.6972166891199107
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.10149966904131512
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.0829095601094917
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.24990961373379394
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1458
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs	1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state	42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_start	false
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copy	false
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power	34
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter	4920
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components	1
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	4.278847204651663
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier)(1)_verbose	false