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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	25.51543185398076
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	41.73875290418972
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.9539977683665652
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.5269712194078501
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	1120
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	47
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	0.11023812293042262
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	0.9523531110032217
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.20822022384691963
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	false
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles	1856
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution	"normal"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state	42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample	97201744
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