Run
10460040

Run 10460040

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 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.MinMaxScaler,s tep_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_components165
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.10005626399700318
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.0002935375496929987
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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.MinMaxScaler,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbosefalse
sklearn.preprocessing._data.MinMaxScaler(1)_copyfalse
sklearn.preprocessing._data.MinMaxScaler(1)_feature_range[0, 1]

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

0.5008 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
0.5658 ± 0.0007
Per class
Cross-validation details (10-fold Crossvalidation)
0.0022 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
0.2295 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.3086 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
0.6914 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
16599
Per class
Cross-validation details (10-fold Crossvalidation)
0.7867 ± 0.0001
Per class
Cross-validation details (10-fold Crossvalidation)
0.6914 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.7225 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5555 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
1.2021 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
0.5008 ± 0.0008
Cross-validation details (10-fold Crossvalidation)