Run 10455176

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,s tep_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	0.00014017731655537654
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	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon	2.627287786953021
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.7628211073716907
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	false
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.7437364717761894
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	1983
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	42
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	0.22480457414129681
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	0.26534464980601485
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.RobustScaler(1)_copy	false
sklearn.preprocessing._data.RobustScaler(1)_quantile_range	[3.602671338290042, 32.779615838029244]
sklearn.preprocessing._data.RobustScaler(1)_with_centering	false
sklearn.preprocessing._data.RobustScaler(1)_with_scaling	true
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,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.RobustScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbose	false