Run
10559712

Run 10559712

Task 9914 (Supervised Classification) wilt Uploaded 21-10-2020 by Claudio Rebelo Sá
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  • openml-python random-subgroups_0.1.1 Sklearn_0.23.1.
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=randomsubgroups._randomsubgroups.RandomSubgroupClassifier)(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.impute._base.SimpleImputer(16)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(16)_copytrue
sklearn.impute._base.SimpleImputer(16)_fill_valuenull
sklearn.impute._base.SimpleImputer(16)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(16)_strategy"mean"
sklearn.impute._base.SimpleImputer(16)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=randomsubgroups._randomsubgroups.RandomSubgroupClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=randomsubgroups._randomsubgroups.RandomSubgroupClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=randomsubgroups._randomsubgroups.RandomSubgroupClassifier)(1)_verbosefalse
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_bootstraptrue
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_intervals_onlytrue
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_depth1
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_features"auto"
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_samplesnull
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_n_bins5
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_n_estimators100
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_n_jobsnull
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_quality_function_weight0.5
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_result_set_size1
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_search_strategy"bestfirst"
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_verbose0

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

Area under ROC curve
0.8227 ± 0.0402
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6457 ± 0.0484
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0921 ± 0.0157
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-12.0854 ± 0.2532
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4817 ± 0.0189
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5317 ± 0.0538
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.947 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5317 ± 0.0538
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
4.7121 ± 0.1698
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6341 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.8071 ± 0.0532
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7326 ± 0.017
Cross-validation details (10-fold Crossvalidation)