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10587823

Run 10587823

Task 359940 (Supervised Regression) yprop_4_1 Uploaded 01-06-2022 by Sergey Redyuk
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Flow

autosklearn.estimators.AutoSklearnRegressor(1)This class implements the regression task.
autosklearn.estimators.AutoSklearnRegressor(1)_dask_clientnull
autosklearn.estimators.AutoSklearnRegressor(1)_delete_tmp_folder_after_terminatetrue
autosklearn.estimators.AutoSklearnRegressor(1)_disable_evaluator_outputfalse
autosklearn.estimators.AutoSklearnRegressor(1)_ensemble_nbest50
autosklearn.estimators.AutoSklearnRegressor(1)_ensemble_size50
autosklearn.estimators.AutoSklearnRegressor(1)_excludenull
autosklearn.estimators.AutoSklearnRegressor(1)_get_smac_object_callbacknull
autosklearn.estimators.AutoSklearnRegressor(1)_get_trials_callbacknull
autosklearn.estimators.AutoSklearnRegressor(1)_includenull
autosklearn.estimators.AutoSklearnRegressor(1)_initial_configurations_via_metalearning25
autosklearn.estimators.AutoSklearnRegressor(1)_load_modelstrue
autosklearn.estimators.AutoSklearnRegressor(1)_logging_confignull
autosklearn.estimators.AutoSklearnRegressor(1)_max_models_on_disc50
autosklearn.estimators.AutoSklearnRegressor(1)_memory_limit10000
autosklearn.estimators.AutoSklearnRegressor(1)_metadata_directorynull
autosklearn.estimators.AutoSklearnRegressor(1)_metricnull
autosklearn.estimators.AutoSklearnRegressor(1)_n_jobs1
autosklearn.estimators.AutoSklearnRegressor(1)_per_run_time_limit30
autosklearn.estimators.AutoSklearnRegressor(1)_resampling_strategy"holdout"
autosklearn.estimators.AutoSklearnRegressor(1)_resampling_strategy_argumentsnull
autosklearn.estimators.AutoSklearnRegressor(1)_scoring_functionsnull
autosklearn.estimators.AutoSklearnRegressor(1)_seed1
autosklearn.estimators.AutoSklearnRegressor(1)_smac_scenario_argsnull
autosklearn.estimators.AutoSklearnRegressor(1)_time_left_for_this_task300
autosklearn.estimators.AutoSklearnRegressor(1)_tmp_foldernull

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.

7 Evaluation measures

Mean prior absolute error
0.0202 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Number of instances
8885
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.0298 ± 0.0048
Cross-validation details (10-fold Crossvalidation)