Run
10454925

Run 10454925

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBin sDiscretizer,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)_alpha0.0007429551199152421
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_class_weightnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_early_stoppingtrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon35.433013833156295
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.1652578207784994
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.3432371124045966
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"invscaling"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"perceptron"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter122
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change15
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"elasticnet"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.49021611041331375
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.05109810590405882
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.894044538936756
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_encode"onehot"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins2
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy"uniform"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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._discretization.KBinsDiscretizer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

16 Evaluation measures

0.5
Per class
Cross-validation details (10-fold Crossvalidation)
± 0.0024
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
3848
Per class
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
1
Cross-validation details (10-fold Crossvalidation)
1 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.7071 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
1.4142 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)