Run
10560635

Run 10560635

Task 9985 (Supervised Classification) first-order-theorem-proving Uploaded 21-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imput er,estimator=sklearn.naive_bayes.GaussianNB)(2)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 to None.
sklearn.preprocessing.imputation.Imputer(54)_axis0
sklearn.preprocessing.imputation.Imputer(54)_copytrue
sklearn.preprocessing.imputation.Imputer(54)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(54)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(54)_verbose0
sklearn.naive_bayes.GaussianNB(21)_priorsnull
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.naive_bayes.GaussianNB)(2)_memorynull
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.naive_bayes.GaussianNB)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]

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

0.6277 ± 0.0166
Per class
Cross-validation details (10-fold Crossvalidation)
0.1408 ± 0.0183
Per class
Cross-validation details (10-fold Crossvalidation)
0.0607 ± 0.0149
Cross-validation details (10-fold Crossvalidation)
0.0586 ± 0.0183
Cross-validation details (10-fold Crossvalidation)
0.2753 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.1731 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
6118
Per class
Cross-validation details (10-fold Crossvalidation)
0.3817 ± 0.0502
Per class
Cross-validation details (10-fold Crossvalidation)
0.1731 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
2.3 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
1.0977 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.5085 ± 0.0047
Cross-validation details (10-fold Crossvalidation)
1.4363 ± 0.0132
Cross-validation details (10-fold Crossvalidation)
0.2368 ± 0.0178
Cross-validation details (10-fold Crossvalidation)