Run 10456305

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.Trunc atedSVD,step_1=sklearn.tree._classes.DecisionTreeClassifier)(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	0.1977272162905731
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.7431855595530982
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	3413
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.04082223739649685
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.09991674298960301
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.32200789900198357
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.23682670324870692
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_algorithm	"arpack"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components	1
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter	96
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state	42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol	2.4019868840687244