Run
10464029

Run 10464029

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.GenericUnivariateSelect,step_1=sklearn.naive_bayes.MultinomialNB)(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.naive_bayes.MultinomialNB(6)_alpha0.0005996822522140146
sklearn.naive_bayes.MultinomialNB(6)_class_priornull
sklearn.naive_bayes.MultinomialNB(6)_fit_priortrue
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_mode"fwe"
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_param0.0960936780047215
sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.naive_bayes.MultinomialNB)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.naive_bayes.MultinomialNB)(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.GenericUnivariateSelect,step_1=sklearn.naive_bayes.MultinomialNB)(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

0.5943 ± 0.0157
Per class
Cross-validation details (10-fold Crossvalidation)
0.4023 ± 0.0101
Per class
Cross-validation details (10-fold Crossvalidation)
0.0103 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
0.0091 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
0.4913 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5552 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.6825 ± 0.0458
Per class
Cross-validation details (10-fold Crossvalidation)
0.5552 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.993 ± 0.002
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4943 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.9938 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.5047 ± 0.0035
Cross-validation details (10-fold Crossvalidation)