Run 10456937

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

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	0.8752266922150194
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	0.10970855870863251
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	651
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.8471063509987152
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.015492724435090624
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.30302316212973773
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.0032045519352732477
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columns	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity	"euclidean"
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	"single"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memory	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters	2
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