Run
10233439

Run 10233439

Task 43 (Supervised Classification) spambase Uploaded 23-07-2019 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)_bandwidth1.0
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)_degree2
sklearn_extra.fast_kernel.FKC_EigenPro(1)_gamma0.01
sklearn_extra.fast_kernel.FKC_EigenPro(1)_kernel"gaussian"
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_state43822
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.

17 Evaluation measures

Area under ROC curve
0.9173 ± 0.0147
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9225 ± 0.0123
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8374 ± 0.0262
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8347 ± 0.026
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0774 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4776 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
4601
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9225 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9226 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9674 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Recall
0.9226 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.162 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2782 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5693 ± 0.0456
Cross-validation details (10-fold Crossvalidation)