Run
10560672

Run 10560672

Task 146817 (Supervised Classification) steel-plates-fault 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

0.7886 ± 0.0225
Per class
Cross-validation details (10-fold Crossvalidation)
0.3999 ± 0.0376
Per class
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.0365
Cross-validation details (10-fold Crossvalidation)
0.4145 ± 0.0318
Cross-validation details (10-fold Crossvalidation)
0.1609 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.459 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.5346 ± 0.0791
Per class
Cross-validation details (10-fold Crossvalidation)
0.459 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
0.7237 ± 0.0334
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.3498 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
1.0492 ± 0.0364
Cross-validation details (10-fold Crossvalidation)
0.5019 ± 0.0229
Cross-validation details (10-fold Crossvalidation)