OpenML
10299422

Run 10299422

Task 22 (Supervised Classification) mfeat-zernike Uploaded 31-07-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklear n_extra.fast_kernel.FKCEigenPro)(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.preprocessing.data.StandardScaler(29)_copytrue
sklearn.preprocessing.data.StandardScaler(29)_with_meantrue
sklearn.preprocessing.data.StandardScaler(29)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(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": "fkceigenpro", "step_name": "fkceigenpro"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)_verbosefalse
sklearn_extra.fast_kernel.FKCEigenPro(1)_bandwidth5
sklearn_extra.fast_kernel.FKCEigenPro(1)_batch_size"auto"
sklearn_extra.fast_kernel.FKCEigenPro(1)_coef01
sklearn_extra.fast_kernel.FKCEigenPro(1)_degree2
sklearn_extra.fast_kernel.FKCEigenPro(1)_gamma0.1
sklearn_extra.fast_kernel.FKCEigenPro(1)_kernel"laplace"
sklearn_extra.fast_kernel.FKCEigenPro(1)_kernel_params"null"
sklearn_extra.fast_kernel.FKCEigenPro(1)_n_components2000
sklearn_extra.fast_kernel.FKCEigenPro(1)_n_epoch2
sklearn_extra.fast_kernel.FKCEigenPro(1)_random_state27533
sklearn_extra.fast_kernel.FKCEigenPro(1)_subsample_size"auto"

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
0.8922 ± 0.0124
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8025 ± 0.0233
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7844 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7971 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0388 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7995 ± 0.0249
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.806 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Recall
0.806 ± 0.0223
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2156 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.197 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6566 ± 0.0385
Cross-validation details (10-fold Crossvalidation)