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9201682

Run 9201682

Task 31 (Supervised Classification) credit-g Uploaded 21-05-2018 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))(3)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(21)_categorical_features[true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, 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
sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)(3)_memorynull
arbok.preprocessing.ConditionalImputer(3)_axis0
arbok.preprocessing.ConditionalImputer(3)_categorical_features[true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true]
arbok.preprocessing.ConditionalImputer(3)_copytrue
arbok.preprocessing.ConditionalImputer(3)_fill_emptynull
arbok.preprocessing.ConditionalImputer(3)_missing_values"NaN"
arbok.preprocessing.ConditionalImputer(3)_strategy"mean"
arbok.preprocessing.ConditionalImputer(3)_strategy_nominal"most_frequent"
arbok.preprocessing.ConditionalImputer(3)_verbose0
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_delete_output_folder_after_terminatetrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_delete_tmp_folder_after_terminatetrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_disable_evaluator_outputfalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_ensemble_nbest50
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_ensemble_size50
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_exclude_estimatorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_exclude_preprocessorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_get_smac_object_callbacknull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_include_estimatorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_include_preprocessorsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_initial_configurations_via_metalearning25
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_ml_memory_limit3072
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_output_foldernull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_per_run_time_limit360
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_refittrue
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_resampling_strategy"holdout"
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_resampling_strategy_argumentsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_retry_on_errorfalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_seed1
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_shared_modefalse
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_smac_scenario_argsnull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_time_left_for_this_task30
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_tmp_foldernull
arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(3)_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.

arff
Trace

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

17 Evaluation measures

Area under ROC curve
0.66 ± 0.0434
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7409 ± 0.0356
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.359 ± 0.0894
Cross-validation details (10-fold Crossvalidation)
KB relative information score
386.9284 ± 8.4217
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.24 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7462 ± 0.0417
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.76 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8818
Cross-validation details (10-fold Crossvalidation)
Recall
0.76 ± 0.033
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5712 ± 0.0785
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4899 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.069 ± 0.0724
Cross-validation details (10-fold Crossvalidation)