Run
10464769

Run 10464769

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 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.QuantileTransf ormer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components148
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkagenull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"svd"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.0030070910754536936
sklearn.preprocessing._data.QuantileTransformer(1)_copyfalse
sklearn.preprocessing._data.QuantileTransformer(1)_ignore_implicit_zerosfalse
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles1856
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution"normal"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample97201744
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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.QuantileTransformer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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

0.4999 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
0.4198
Per class
-0.0001
-0.0187 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
0.5009 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4692 ± 0.0431
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.5052
Per class
0.4692 ± 0.0431
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
1.0125 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5011 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
1.0076 ± 0.0029
Cross-validation details (10-fold Crossvalidation)
0.4999
Cross-validation details (10-fold Crossvalidation)