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Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeat ures,step_1=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	3.87434982553317e-05
sklearn.svm._classes.SVC(4)_break_ties	true
sklearn.svm._classes.SVC(4)_cache_size	200
sklearn.svm._classes.SVC(4)_class_weight	null
sklearn.svm._classes.SVC(4)_coef0	-27.794776952313295
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	5
sklearn.svm._classes.SVC(4)_gamma	0.012340433003027526
sklearn.svm._classes.SVC(4)_kernel	"poly"
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	0.016599340345519575
sklearn.svm._classes.SVC(4)_verbose	false
sklearn.preprocessing._data.PolynomialFeatures(1)_degree	3
sklearn.preprocessing._data.PolynomialFeatures(1)_include_bias	true
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_only	false
sklearn.preprocessing._data.PolynomialFeatures(1)_order	"F"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.svm._classes.SVC)(1)_verbose	false