Run
10463762

Run 10463762

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.decomposition._factor_analysis.Fac torAnalysis,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate0.33422817000872523
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators220
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
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.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier)(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
0.7996 ± 0.0099
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7227 ± 0.0076
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4383 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0061 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4966 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7263 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7271 ± 0.0081
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7263 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0036 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4966 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9985 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7158 ± 0.0075
Cross-validation details (10-fold Crossvalidation)