Run 10463711

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.linear_model._stochastic_gradient.SGDClassifier)(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.linear_model._stochastic_gradient.SGDClassifier(2)_alpha	0.09457286923128413
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_average	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_class_weight	null
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_early_stopping	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon	34.59764827154631
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.843156385186826
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.6973022353344536
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"squared_epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	2214
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	5
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs	1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty	"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t	0.36885906931414
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state	42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffle	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol	0.2994862792163115
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose	0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_start	false
sklearn.preprocessing._data.PolynomialFeatures(1)_degree	3
sklearn.preprocessing._data.PolynomialFeatures(1)_include_bias	false
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_only	false
sklearn.preprocessing._data.PolynomialFeatures(1)_order	"C"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(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.linear_model._stochastic_gradient.SGDClassifier)(1)_verbose	false