Run 10459992

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=skle arn.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	false
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.02249290535473114
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	1377
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.576175529102978
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.395460451204353
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.15537121585450006
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.20850140873144327
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.299639851054875
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1182
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._pca.PCA(1)_copy	false
sklearn.decomposition._pca.PCA(1)_iterated_power	250
sklearn.decomposition._pca.PCA(1)_n_components	3
sklearn.decomposition._pca.PCA(1)_random_state	42
sklearn.decomposition._pca.PCA(1)_svd_solver	"randomized"
sklearn.decomposition._pca.PCA(1)_tol	2.1320885187686316
sklearn.decomposition._pca.PCA(1)_whiten	false
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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._pca.PCA,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false