Run 10464409

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 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	0.619034171508914
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	0.3905703704804881
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	11836
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.15995866025871253
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.3333747394370403
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.40029197317414866
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.1896650964053892
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	"single"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memory	null
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters	4
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func	{"oml-python:serialized_object": "function", "value": "numpy.median"}
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