Run
10425075

Run 10425075

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 05-12-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.cl asses.SVC)(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"median"
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.svm.classes.SVC(31)_C0.40314521746072823
sklearn.svm.classes.SVC(31)_cache_size200
sklearn.svm.classes.SVC(31)_class_weightnull
sklearn.svm.classes.SVC(31)_coef00.2614617204682821
sklearn.svm.classes.SVC(31)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(31)_degree1
sklearn.svm.classes.SVC(31)_gamma0.00010783726568112908
sklearn.svm.classes.SVC(31)_kernel"poly"
sklearn.svm.classes.SVC(31)_max_iter-1
sklearn.svm.classes.SVC(31)_probabilitytrue
sklearn.svm.classes.SVC(31)_random_state1
sklearn.svm.classes.SVC(31)_shrinkingtrue
sklearn.svm.classes.SVC(31)_tol0.001
sklearn.svm.classes.SVC(31)_verbosefalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(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": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)_verbosefalse

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

0.8727 ± 0.0107
Per class
Cross-validation details (10-fold Crossvalidation)
0.6806 ± 0.0344
Per class
Cross-validation details (10-fold Crossvalidation)
0.5882 ± 0.0459
Cross-validation details (10-fold Crossvalidation)
0.5103 ± 0.0135
Cross-validation details (10-fold Crossvalidation)
0.1456 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.6821 ± 0.0345
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.6855 ± 0.035
Per class
Cross-validation details (10-fold Crossvalidation)
0.6821 ± 0.0345
Cross-validation details (10-fold Crossvalidation)
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
0.6549 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.2572 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
0.7716 ± 0.0136
Cross-validation details (10-fold Crossvalidation)
0.6765 ± 0.0482
Cross-validation details (10-fold Crossvalidation)