Run
10462229

Run 10462229

Task 3891 (Supervised Classification) gina_agnostic 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._variance_thresh old.VarianceThreshold,step_1=sklearn.discriminant_analysis.LinearDiscrimina ntAnalysis)(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_components53
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.48445194138683345
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.03321375873781218
sklearn.feature_selection._variance_threshold.VarianceThreshold(2)_threshold0.0
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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.feature_selection._variance_threshold.VarianceThreshold,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

0.9347 ± 0.0147
Per class
Cross-validation details (10-fold Crossvalidation)
0.8624 ± 0.0154
Per class
Cross-validation details (10-fold Crossvalidation)
0.7247 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
0.6959 ± 0.0289
Cross-validation details (10-fold Crossvalidation)
0.1558 ± 0.0142
Cross-validation details (10-fold Crossvalidation)
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
0.8625 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
3468
Per class
Cross-validation details (10-fold Crossvalidation)
0.8628 ± 0.0157
Per class
Cross-validation details (10-fold Crossvalidation)
0.8625 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.3116 ± 0.0284
Cross-validation details (10-fold Crossvalidation)
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
0.3287 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
0.6575 ± 0.0441
Cross-validation details (10-fold Crossvalidation)
0.8621 ± 0.0153
Cross-validation details (10-fold Crossvalidation)