Run
10376023

Run 10376023

Task 3022 (Supervised Classification) vowel Uploaded 25-08-2019 by Heinrich Peters
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num =sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler ),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneH otEncoder)),svc=sklearn.svm.classes.SVC)(2)Automatically created scikit-learn flow.
sklearn.impute._base.SimpleImputer(3)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(3)_copytrue
sklearn.impute._base.SimpleImputer(3)_fill_valuenull
sklearn.impute._base.SimpleImputer(3)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(3)_strategy"median"
sklearn.impute._base.SimpleImputer(3)_verbose0
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)_C1e-10
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)_gamma0.1
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_state33833
sklearn.svm.classes.SVC(32)_shrinkingtrue
sklearn.svm.classes.SVC(32)_tol0.001
sklearn.svm.classes.SVC(32)_verbosefalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=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(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=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(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [false, false, true, true, true, true, true, true, true, true, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [true, true, false, false, false, false, false, false, false, false, false, false]}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder))(2)_verbosefalse
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)(2)_memorynull
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehot", "step_name": "onehot"}}]
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(12)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_categoriesnull
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"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(12)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_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

Area under ROC curve
0.8517 ± 0.0157
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7284 ± 0.0282
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7033 ± 0.0314
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7196 ± 0.0297
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.049 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1653
Cross-validation details (10-fold Crossvalidation)
Number of instances
990
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.75 ± 0.0329
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7303 ± 0.0286
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.4594
Cross-validation details (10-fold Crossvalidation)
Recall
0.7303 ± 0.0286
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2967 ± 0.0314
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2875
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2214 ± 0.0118
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7703 ± 0.0409
Cross-validation details (10-fold Crossvalidation)