Run
10389787

Run 10389787

Task 23 (Supervised Classification) cmc Uploaded 27-08-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardSc aler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklea rn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.data.StandardScaler(30)_copytrue
sklearn.preprocessing.data.StandardScaler(30)_with_meantrue
sklearn.preprocessing.data.StandardScaler(30)_with_stdtrue
sklearn.svm.classes.SVC(32)_C1.0
sklearn.svm.classes.SVC(32)_cache_size200
sklearn.svm.classes.SVC(32)_class_weightnull
sklearn.svm.classes.SVC(32)_coef00.0
sklearn.svm.classes.SVC(32)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(32)_degree3
sklearn.svm.classes.SVC(32)_gamma"scale"
sklearn.svm.classes.SVC(32)_kernel"rbf"
sklearn.svm.classes.SVC(32)_max_iter-1
sklearn.svm.classes.SVC(32)_probabilityfalse
sklearn.svm.classes.SVC(32)_random_state3
sklearn.svm.classes.SVC(32)_shrinkingtrue
sklearn.svm.classes.SVC(32)_tol0.001
sklearn.svm.classes.SVC(32)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(12)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(12)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(12)_handle_unknown"error"
sklearn.preprocessing._encoders.OneHotEncoder(12)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(1)_steps[{"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(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler", "argument_1": [false, true, true, false, true, true, true, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": [false, true, true, false, true, true, true, true, true]}}]
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(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.

17 Evaluation measures

Area under ROC curve
0.579 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.458 ± 0.0412
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1608 ± 0.0613
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2317 ± 0.0561
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3571 ± 0.0267
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4595 ± 0.0441
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4644 ± 0.04
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Recall
0.4644 ± 0.04
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8289 ± 0.062
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
0.5976 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2876 ± 0.0488
Cross-validation details (10-fold Crossvalidation)