Run 10454217

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 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	3.327314088599713e-05
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	13.739574650802512
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta0	0.30092939629256626
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercept	true
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio	0.49126120127641326
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate	"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss	"hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter	1016
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change	100
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs	1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty	"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t	0.5323050386759196
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	0.022931017153756054
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.QuantileTransformer(1)_copy	false
sklearn.preprocessing._data.QuantileTransformer(1)_ignore_implicit_zeros	false
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles	1168
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution	"normal"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state	42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample	47168686
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