Run 10455996

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

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	1.3774329841744097e-06
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	39.73972054256296
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.9682984173977274
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.44424805092175174
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	1314
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	97
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	0.8596526119123871
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	0.40313095534508253
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.7861177832275108
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