Run 10456816

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.de composition._truncated_svd.TruncatedSVD,step_2=sklearn.tree._classes.Decisi onTreeClassifier)(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	0.2576108444341577
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	0.10160076895563808
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	720
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.6033884853962409
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.2937639932030274
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.13913397368341177
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.04374354499313449
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columns	null
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_algorithm	"arpack"
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_components	4
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_n_iter	42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_random_state	42
sklearn.decomposition._truncated_svd.TruncatedSVD(1)_tol	2.306415177071679
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._truncated_svd.TruncatedSVD,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._truncated_svd.TruncatedSVD,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=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._truncated_svd.TruncatedSVD,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false