Run
10455860

Run 10455860

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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.discriminant_analysis.LinearDiscrimin antAnalysis)(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_components338
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.08246152812388863
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.004721366569388596
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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._univariate_selection.SelectPercentile,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbosefalse
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile50.631243791965574
sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._mutual_info.mutual_info_classif"}

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.

16 Evaluation measures

0.8027 ± 0.0374
Per class
Cross-validation details (10-fold Crossvalidation)
-0.3258 ± 0.058
Cross-validation details (10-fold Crossvalidation)
0.0937 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
4839
Per class
Cross-validation details (10-fold Crossvalidation)
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.9162 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.2205 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.9759 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)