Run
10463625

Run 10463625

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 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._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)_alpha1.3439158446797803
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)_epsilon12.409398781692285
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.23402188667578144
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_intercepttrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.08813837591870469
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"modified_huber"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1505
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change92
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l1"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.29948651489908307
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.2385655405068037
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.8743714322002203
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_degree3
sklearn.preprocessing._data.PolynomialFeatures(1)_include_biastrue
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_onlyfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_order"F"
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
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)_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.

18 Evaluation measures

Area under ROC curve
0.5142 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4749 ± 0.0437
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0304 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0923 ± 0.0698
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.447 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.553 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.536 ± 0.0361
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.553 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9035 ± 0.0695
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6686 ± 0.0246
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3442 ± 0.0495
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5142 ± 0.0057
Cross-validation details (10-fold Crossvalidation)