Run
10559714

Run 10559714

Task 31 (Supervised Classification) credit-g Uploaded 21-10-2020 by Claudio Rebelo Sá
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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_onlyfalse
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_depth1
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_features"auto"
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_max_samplesnull
randomsubgroups._randomsubgroups.RandomSubgroupClassifier(1)_n_bins10
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
0.73 ± 0.0346
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7231 ± 0.0372
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3597 ± 0.0808
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-1.2566 ± 0.0278
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.462 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.717 ± 0.0397
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7335 ± 0.0338
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.717 ± 0.0397
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0995 ± 0.0149
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6018 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3131 ± 0.0193
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6893 ± 0.0407
Cross-validation details (10-fold Crossvalidation)