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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.StandardScaler ,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.preprocessing._data.StandardScaler(1)_copy	false
sklearn.preprocessing._data.StandardScaler(1)_with_mean	false
sklearn.preprocessing._data.StandardScaler(1)_with_std	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha	4.163105705469674e-07
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	1.4285736743370305
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.6625656642588725
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	742
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	34
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	0.9018348941414647
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	0.6484533158121799
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.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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.StandardScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbose	false