Run
10560650

Run 10560650

Task 9960 (Supervised Classification) wall-robot-navigation Uploaded 21-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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

0.7859 ± 0.014
Per class
Cross-validation details (10-fold Crossvalidation)
0.5298 ± 0.0228
Per class
Cross-validation details (10-fold Crossvalidation)
0.3601 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
0.4146 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
0.2364 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
0.3312 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.526 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
5456
Per class
Cross-validation details (10-fold Crossvalidation)
0.6265 ± 0.0245
Per class
Cross-validation details (10-fold Crossvalidation)
0.526 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
1.7146 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
0.7137 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
0.4069 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.4371 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
1.0741 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
0.6639 ± 0.0207
Cross-validation details (10-fold Crossvalidation)