Run 10463526

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._fastica.FastICA,step_2=sklearn.tree._classes.Deci sionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.6860019556769771
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	47
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.13840506939279792
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	198
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.23445184674784447
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.0789662967597115
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.25227793832752815
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.015910720254541044
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.decomposition._fastica.FastICA(1)_algorithm	"deflation"
sklearn.decomposition._fastica.FastICA(1)_fun	"exp"
sklearn.decomposition._fastica.FastICA(1)_fun_args	null
sklearn.decomposition._fastica.FastICA(1)_max_iter	776
sklearn.decomposition._fastica.FastICA(1)_n_components	6
sklearn.decomposition._fastica.FastICA(1)_random_state	42
sklearn.decomposition._fastica.FastICA(1)_tol	0.07603576665611152
sklearn.decomposition._fastica.FastICA(1)_w_init	null
sklearn.decomposition._fastica.FastICA(1)_whiten	true
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._fastica.FastICA,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.tree._classes.DecisionTreeClassifier)(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._fastica.FastICA,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false