Run 10454577

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeat ures,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	3.247055461469581e-07
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	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon	45.73347728773878
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.9262244700725233
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"optimal"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	1856
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	94
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	0.020721254628516126
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	0.3374501726486753
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction	0.46071249892353927
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose	0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_start	false
sklearn.preprocessing._data.PolynomialFeatures(1)_degree	2
sklearn.preprocessing._data.PolynomialFeatures(1)_include_bias	false
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_only	false
sklearn.preprocessing._data.PolynomialFeatures(1)_order	"C"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,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.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbose	false