Run 10461884

Task 3891 (Supervised Classification) gina_agnostic Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAggl omeration,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	0.9003332211551632
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	321
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.9264398432264633
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	132
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.06923455186678225
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.33037656218423395
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.35297875782509414
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.13129913806421534
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity	"l1"
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	"complete"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memory	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters	868
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func	{"oml-python:serialized_object": "function", "value": "numpy.amax"}
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false