Run 10437796

Task 2273 (Supervised Classification) meta_batchincremental.arff Uploaded 03-03-2020 by Fares Gaaloul
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sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical .FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTree Classifier)(2)	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 to None.
sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_memory	null
sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "featureagglomeration", "step_name": "featureagglomeration"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_affinity	"manhattan"
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_compute_full_tree	"auto"
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_connectivity	null
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_linkage	"average"
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_memory	null
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_n_clusters	2
sklearn.cluster.hierarchical.FeatureAgglomeration(4)_pooling_func	{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.tree.tree.DecisionTreeClassifier(64)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(64)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(64)_max_depth	10
sklearn.tree.tree.DecisionTreeClassifier(64)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(64)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(64)_min_impurity_decrease	0.0
sklearn.tree.tree.DecisionTreeClassifier(64)_min_impurity_split	null
sklearn.tree.tree.DecisionTreeClassifier(64)_min_samples_leaf	13
sklearn.tree.tree.DecisionTreeClassifier(64)_min_samples_split	16
sklearn.tree.tree.DecisionTreeClassifier(64)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(64)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(64)_random_state	22408
sklearn.tree.tree.DecisionTreeClassifier(64)_splitter	"best"