Run 10460338

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 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.cl uster._agglomerative.FeatureAgglomeration,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	0.16369446744995364
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	18
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.7136123319107945
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	3345
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.7955589159810957
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.03221957748969952
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.3699516066730585
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.10375509874211353
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.cluster._agglomerative.FeatureAgglomeration(1)_affinity	"cosine"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_tree	true
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivity	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_threshold	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage	"average"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memory	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters	15
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func	{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,step_2=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false