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9201708

Run 9201708

Task 146240 (Supervised Classification) parity5 Uploaded 24-05-2018 by Jeroen van Hoof
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arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline (conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sk learn.preprocessing.data.OneHotEncoder))(5)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(21)_categorical_features[true, true, true, true, true]
sklearn.preprocessing.data.OneHotEncoder(21)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(21)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(21)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(21)_sparsefalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_delete_output_folder_after_terminatetrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_delete_tmp_folder_after_terminatetrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_disable_evaluator_outputfalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_ensemble_nbest50
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_ensemble_size50
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_exclude_estimatorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_exclude_preprocessorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_get_smac_object_callbacknull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_include_estimatorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_include_preprocessorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_initial_configurations_via_metalearning25
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_ml_memory_limit3072
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_output_foldernull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_per_run_time_limit20
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_refittrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_resampling_strategy"holdout"
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_resampling_strategy_argumentsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_retry_on_errorfalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_seed1
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_shared_modefalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_smac_scenario_argsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_time_left_for_this_task20
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_tmp_foldernull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(5)_verbosetrue
sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)(6)_memorynull
arbok.preprocessing.ConditionalImputer(6)_axis0
arbok.preprocessing.ConditionalImputer(6)_categorical_features[true, true, true, true, true]
arbok.preprocessing.ConditionalImputer(6)_copytrue
arbok.preprocessing.ConditionalImputer(6)_fill_emptynull
arbok.preprocessing.ConditionalImputer(6)_missing_values"NaN"
arbok.preprocessing.ConditionalImputer(6)_strategy"mean"
arbok.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
arbok.preprocessing.ConditionalImputer(6)_verbose0

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

0.5 ± 0.1972
Per class
Cross-validation details (10-fold Crossvalidation)
0.4921 ± 0.3333
Per class
Cross-validation details (10-fold Crossvalidation)
± 0.3843
Cross-validation details (10-fold Crossvalidation)
± 1.3333
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.2222
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
32
Per class
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.3469
Per class
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.2222
Cross-validation details (10-fold Crossvalidation)
1
Cross-validation details (10-fold Crossvalidation)
0.5 ± 0.2222
Per class
Cross-validation details (10-fold Crossvalidation)
1 ± 0.4444
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.7071 ± 0.253
Cross-validation details (10-fold Crossvalidation)
1.4142 ± 0.5059
Cross-validation details (10-fold Crossvalidation)