Run
10463910

Run 10463910

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.feature_selection._univariate_sele ction.SelectPercentile,step_1=sklearn.linear_model._stochastic_gradient.SGD Classifier)(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)_alpha4.599287653681225e-05
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_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon36.872283606963904
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.09127909791502933
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.9746261020445373
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"constant"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"squared_hinge"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter1709
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change8
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_t0.7820065381149371
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.2849550557039144
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.feature_selection._univariate_selection.SelectPercentile(1)_percentile22.333941173119946
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._mutual_info.mutual_info_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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.feature_selection._univariate_selection.SelectPercentile,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
0.4946 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.501 ± 0.0487
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0109 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0084 ± 0.0965
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4966 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5034 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4998 ± 0.1587
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5034 ± 0.0476
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0037 ± 0.0961
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.7047 ± 0.0335
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4168 ± 0.0674
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4946 ± 0.0082
Cross-validation details (10-fold Crossvalidation)