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10417329

Run 10417329

Task 23 (Supervised Classification) cmc Uploaded 05-11-2019 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)(3)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.svm.classes.SVC(36)_C0.07077231909653779
sklearn.svm.classes.SVC(36)_cache_size200
sklearn.svm.classes.SVC(36)_class_weightnull
sklearn.svm.classes.SVC(36)_coef00.9005967890758899
sklearn.svm.classes.SVC(36)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(36)_degree4
sklearn.svm.classes.SVC(36)_gamma2.867595836610148
sklearn.svm.classes.SVC(36)_kernel"poly"
sklearn.svm.classes.SVC(36)_max_iter-1
sklearn.svm.classes.SVC(36)_probabilityfalse
sklearn.svm.classes.SVC(36)_random_state1
sklearn.svm.classes.SVC(36)_shrinkingtrue
sklearn.svm.classes.SVC(36)_tol0.001
sklearn.svm.classes.SVC(36)_verbosefalse
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)(3)_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)(3)_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)(3)_verbosefalse
sklearn.impute._base.SimpleImputer(7)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(7)_copytrue
sklearn.impute._base.SimpleImputer(7)_fill_valuenull
sklearn.impute._base.SimpleImputer(7)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(7)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(7)_verbose0
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(4)_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))(4)_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))(4)_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))(4)_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))(4)_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))(4)_verbosefalse
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_memorynull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_verbosefalse
sklearn.preprocessing.data.StandardScaler(33)_copytrue
sklearn.preprocessing.data.StandardScaler(33)_with_meantrue
sklearn.preprocessing.data.StandardScaler(33)_with_stdtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(4)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(14)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(14)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(14)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(14)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(14)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(14)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(14)_sparsetrue

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

0.5774 ± 0.023
Per class
Cross-validation details (10-fold Crossvalidation)
0.4515 ± 0.0308
Per class
Cross-validation details (10-fold Crossvalidation)
0.1531 ± 0.047
Cross-validation details (10-fold Crossvalidation)
0.216 ± 0.0446
Cross-validation details (10-fold Crossvalidation)
0.3657 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
1473
Per class
Cross-validation details (10-fold Crossvalidation)
0.4523 ± 0.0304
Per class
Cross-validation details (10-fold Crossvalidation)
0.4515 ± 0.0311
Cross-validation details (10-fold Crossvalidation)
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.4515 ± 0.0311
Per class
Cross-validation details (10-fold Crossvalidation)
0.8488 ± 0.0479
Cross-validation details (10-fold Crossvalidation)
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.6047 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
1.303 ± 0.0366
Cross-validation details (10-fold Crossvalidation)