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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.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	0.004358112894747891
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	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon	44.36537517185613
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.23446253121740518
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.9005859026958034
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"squared_epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	955
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	82
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs	1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty	"l1"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t	0.34126045078624584
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	0.9242870884655281
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.014793478439281027
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