Run 10463963

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	4.665073846157752
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_average	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_class_weight	null
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_early_stopping	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon	33.87645445829498
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.3059308651715594
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.290643476253098
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"squared_loss"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	2309
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	75
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	0.26535362830041714
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state	42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffle	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol	0.5095672338218915
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.25350061092475284
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	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.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