Run 10456360

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	0.3728245628615027
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.4082648092238655
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2157
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.06407782034483611
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.20030107274310524
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.04208685391636453
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.26419982780545237
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	"randomized"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components	3
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter	84
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state	42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol	0.0