Run
10430246

Run 10430246

Task 23 (Supervised Classification) cmc Uploaded 10-12-2019 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)_C0.10624363454623975
sklearn.svm.classes.SVC(31)_cache_size200
sklearn.svm.classes.SVC(31)_class_weightnull
sklearn.svm.classes.SVC(31)_coef00.4927944639918054
sklearn.svm.classes.SVC(31)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(31)_degree2
sklearn.svm.classes.SVC(31)_gamma0.4389095597471219
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.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": [true, false, false, true, false, false, false, false, false]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, true, true, false, true, true, true, true, true]}}]
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
0.727 ± 0.0313
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5374 ± 0.0358
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.282 ± 0.0553
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2017 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3735 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5458 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5385 ± 0.0409
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5458 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8669 ± 0.0181
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4301 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9267 ± 0.0213
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5094 ± 0.038
Cross-validation details (10-fold Crossvalidation)