Run
10363260

Run 10363260

Task 32 (Supervised Classification) pendigits Uploaded 22-08-2019 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)_C0.3077938509958907
sklearn.svm.classes.SVC(31)_cache_size200
sklearn.svm.classes.SVC(31)_class_weightnull
sklearn.svm.classes.SVC(31)_coef0-0.047163337896294344
sklearn.svm.classes.SVC(31)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(31)_degree4
sklearn.svm.classes.SVC(31)_gamma3.8851078332963743e-05
sklearn.svm.classes.SVC(31)_kernel"rbf"
sklearn.svm.classes.SVC(31)_max_iter-1
sklearn.svm.classes.SVC(31)_probabilityfalse
sklearn.svm.classes.SVC(31)_random_state40038
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

Area under ROC curve
0.5312 ± 0.0272
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0624 ± 0.0544
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.119 ± 0.0503
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.168 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
10992
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.1599 ± 0.0488
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Recall
0.1599 ± 0.0488
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9336 ± 0.0542
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4099 ± 0.0118
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3664 ± 0.0395
Cross-validation details (10-fold Crossvalidation)