OpenML
10373479

Run 10373479

Task 28 (Supervised Classification) optdigits Uploaded 24-08-2019 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"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.svm.classes.SVC(31)_C0.04627508381299772
sklearn.svm.classes.SVC(31)_cache_size200
sklearn.svm.classes.SVC(31)_class_weightnull
sklearn.svm.classes.SVC(31)_coef0-0.563230253602446
sklearn.svm.classes.SVC(31)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(31)_degree3
sklearn.svm.classes.SVC(31)_gamma0.00011061707084433878
sklearn.svm.classes.SVC(31)_kernel"sigmoid"
sklearn.svm.classes.SVC(31)_max_iter-1
sklearn.svm.classes.SVC(31)_probabilityfalse
sklearn.svm.classes.SVC(31)_random_state25063
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.

15 Evaluation measures

0.5109 ± 0.0232
Per class
Cross-validation details (10-fold Crossvalidation)
0.0218 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
0.0803 ± 0.0437
Cross-validation details (10-fold Crossvalidation)
0.1757 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
5620
Per class
Cross-validation details (10-fold Crossvalidation)
0.1214 ± 0.042
Cross-validation details (10-fold Crossvalidation)
3.3218 ± 0
Cross-validation details (10-fold Crossvalidation)
0.1214 ± 0.042
Per class
Cross-validation details (10-fold Crossvalidation)
0.9763 ± 0.0467
Cross-validation details (10-fold Crossvalidation)
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4192 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
1.3973 ± 0.034
Cross-validation details (10-fold Crossvalidation)