Run
10431466

Run 10431466

Task 29 (Supervised Classification) credit-approval Uploaded 11-12-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num =sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standa rdScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing ._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)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)_C64.0
sklearn.svm.classes.SVC(31)_cache_size200
sklearn.svm.classes.SVC(31)_class_weightnull
sklearn.svm.classes.SVC(31)_coef00.0
sklearn.svm.classes.SVC(31)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(31)_degree3
sklearn.svm.classes.SVC(31)_gamma0.0078125
sklearn.svm.classes.SVC(31)_kernel"rbf"
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.preprocessing._encoders.OneHotEncoder(11)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(11)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(11)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_sparsetrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [false, true, true, false, false, false, false, true, false, false, true, false, false, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [true, false, false, true, true, true, true, false, true, true, false, true, true, false, false]}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_verbosefalse
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_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

Area under ROC curve
0.9181 ± 0.0232
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8655 ± 0.0452
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7284 ± 0.0898
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5918 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.218 ± 0.0197
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.494 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8652 ± 0.0453
Cross-validation details (10-fold Crossvalidation)
Number of instances
690
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8666 ± 0.0429
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8652 ± 0.0453
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9912 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4413 ± 0.0399
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.497 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.324 ± 0.0316
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.652 ± 0.0636
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.866 ± 0.0438
Cross-validation details (10-fold Crossvalidation)