Run
10463645

Run 10463645

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.StandardScaler ,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.preprocessing._data.StandardScaler(1)_copyfalse
sklearn.preprocessing._data.StandardScaler(1)_with_meanfalse
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha1.3065563071843602e-06
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_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon0.10724954503817707
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.2857168968389962
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.32033774258713454
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"epsilon_insensitive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter2454
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change58
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_t0.4239006740599257
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shufflefalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.5262406377665825
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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.StandardScaler,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
0.4992 ± 0.0027
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5006 ± 0.0667
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0015 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0167 ± 0.1115
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5007 ± 0.0549
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4993 ± 0.0549
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5045 ± 0.2997
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4993 ± 0.0549
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0119 ± 0.111
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7076 ± 0.0389
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4226 ± 0.0781
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4992 ± 0.0027
Cross-validation details (10-fold Crossvalidation)