Run
10464797

Run 10464797

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 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.decomposition._factor_analysis.Fac torAnalysis,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)_alpha0.021382863969845387
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagetrue
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)_epsilon5.2924501615734645
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.2470263565274223
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.5760759260971872
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1533
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change95
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.37029918734247236
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.597280916294797
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.5455733117232707
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copyfalse
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power19
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter3186
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components13
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_noise_variance_initnull
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_random_state42
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_svd_method"randomized"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol0.7134622102625292
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,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.decomposition._factor_analysis.FactorAnalysis,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)
0.0887 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.4488 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5512 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.5512 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.9071 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.6699 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
1.3469 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)