OpenML
10229126

Run 10229126

Task 9971 (Supervised Classification) ilpd Uploaded 08-07-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklea rn_extra.fast_kernel.FKC_EigenPro)(1)Automatically created scikit-learn flow.
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn_extra.fast_kernel.FKC_EigenPro(1)_bandwidth10000.0
sklearn_extra.fast_kernel.FKC_EigenPro(1)_batch_size"auto"
sklearn_extra.fast_kernel.FKC_EigenPro(1)_coef01
sklearn_extra.fast_kernel.FKC_EigenPro(1)_degree4
sklearn_extra.fast_kernel.FKC_EigenPro(1)_gamma0.01
sklearn_extra.fast_kernel.FKC_EigenPro(1)_kernel"laplace"
sklearn_extra.fast_kernel.FKC_EigenPro(1)_kernel_paramsnull
sklearn_extra.fast_kernel.FKC_EigenPro(1)_n_components1000
sklearn_extra.fast_kernel.FKC_EigenPro(1)_n_epoch2
sklearn_extra.fast_kernel.FKC_EigenPro(1)_random_state4009
sklearn_extra.fast_kernel.FKC_EigenPro(1)_subsample_size"auto"
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "fkc_eigenpro", "step_name": "fkc_eigenpro"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)_verbosefalse
sklearn.preprocessing.data.StandardScaler(29)_copytrue
sklearn.preprocessing.data.StandardScaler(29)_with_meantrue
sklearn.preprocessing.data.StandardScaler(29)_with_stdtrue

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.

15 Evaluation measures

Area under ROC curve
0.5
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2414 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2864 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4091 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Number of instances
583
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7136 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8641 ± 0.01
Cross-validation details (10-fold Crossvalidation)
Recall
0.7136 ± 0.0076
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7002 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4521 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5352 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1838 ± 0.0064
Cross-validation details (10-fold Crossvalidation)