Run 10460564

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 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.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative .FeatureAgglomeration,step_2=sklearn.svm._classes.SVC)(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.svm._classes.SVC(4)_C	13091.802309787285
sklearn.svm._classes.SVC(4)_break_ties	false
sklearn.svm._classes.SVC(4)_cache_size	200
sklearn.svm._classes.SVC(4)_class_weight	null
sklearn.svm._classes.SVC(4)_coef0	9994.289426925354
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	3
sklearn.svm._classes.SVC(4)_gamma	0.0341519109974776
sklearn.svm._classes.SVC(4)_kernel	"sigmoid"
sklearn.svm._classes.SVC(4)_max_iter	-1
sklearn.svm._classes.SVC(4)_probability	false
sklearn.svm._classes.SVC(4)_random_state	42
sklearn.svm._classes.SVC(4)_shrinking	false
sklearn.svm._classes.SVC(4)_tol	2.7409632943154304e-05
sklearn.svm._classes.SVC(4)_verbose	false
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	6
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func	{"oml-python:serialized_object": "function", "value": "numpy.amax"}
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=sklearn.svm._classes.SVC)(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.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=sklearn.svm._classes.SVC)(1)_verbose	false