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Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransf ormer,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	5.599498000516714
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	7.807828079664899
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.6803151287122521
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.653842733333129
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	194
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	61
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs	1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty	"elasticnet"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t	0.026402660908359993
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	0.2444287645795984
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.QuantileTransformer(1)_copy	false
sklearn.preprocessing._data.QuantileTransformer(1)_ignore_implicit_zeros	true
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles	1911
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution	"uniform"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state	42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample	55468519
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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.QuantileTransformer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbose	false