OpenML-CC18: electricity
Best predictive accuracy per machine-learning flow on the electricity classification task from the OpenML-CC18 suite.
Community results for the electricity classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/219). Each subject is a flow — a specific algorithm or pipeline — shown with the best predictive accuracy recorded for it on this task in OpenML's public evaluation listing, under the task's fixed estimation procedure.
Results
predictive_accuracy per subject over 40 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9501 | 0.9501 | 0.9501 | 0.9501 | 0.9501 | 0.9501 | 1 |
| weka.AdaBoostM1_J48(2) | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 1 |
| mlr.classif.xgboost(7) | 0.9458 | 0.9458 | 0.9458 | 0.9458 | 0.9458 | 0.9458 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9453 | 0.9453 | 0.9453 | 0.9453 | 0.9453 | 0.9453 | 1 |
| mlr.classif.xgboost(6) | 0.9431 | 0.9431 | 0.9431 | 0.9431 | 0.9431 | 0.9431 | 1 |
| mlr.classif.xgboost(11) | 0.9411 | 0.9411 | 0.9411 | 0.9411 | 0.9411 | 0.9411 | 1 |
| mlr.classif.xgboost(9) | 0.9397 | 0.9397 | 0.9397 | 0.9397 | 0.9397 | 0.9397 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.9383 | 0.9383 | 0.9383 | 0.9383 | 0.9383 | 0.9383 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.9382 | 0.9382 | 0.9382 | 0.9382 | 0.9382 | 0.9382 | 1 |
| weka.FilteredClassifier_AdaBoostM1_J48(1) | 0.9381 | 0.9381 | 0.9381 | 0.9381 | 0.9381 | 0.9381 | 1 |
| weka.AdaBoostM1_J48(3) | 0.9374 | 0.9374 | 0.9374 | 0.9374 | 0.9374 | 0.9374 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9367 | 0.9367 | 0.9367 | 0.9367 | 0.9367 | 0.9367 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 1 |
| weka.Bagging_LMT(2) | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 0.9357 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9338 | 0.9338 | 0.9338 | 0.9338 | 0.9338 | 0.9338 | 1 |
| mlr.classif.ranger(16) | 0.9335 | 0.9335 | 0.9335 | 0.9335 | 0.9335 | 0.9335 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.9334 | 0.9334 | 0.9334 | 0.9334 | 0.9334 | 0.9334 | 1 |
| weka.FilteredClassifier_AdaBoostM1_LMT(1) | 0.9331 | 0.9331 | 0.9331 | 0.9331 | 0.9331 | 0.9331 | 1 |
| weka.AdaBoostM1_LMT(3) | 0.9328 | 0.9328 | 0.9328 | 0.9328 | 0.9328 | 0.9328 | 1 |
| mlr.classif.ranger(9) | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 1 |
| mlr.classif.ranger(15) | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 0.9318 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 1 |
| weka.FilteredClassifier_MultiBoostAB_J48(1) | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 0.9313 | 1 |
| weka.RandomForest(12) | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 1 |
| weka.RandomForest(9) | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 0.9311 | 1 |
…and 15 more subjects.
Metrics
- predictive_accuracy — recorded on each measurement. Best predictive accuracy this flow achieved on the task: the fraction of test instances classified correctly, from 0 to 1. Higher is better
Subjects (40)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.xgboost(11)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(7)
- mlr.classif.xgboost(9)
- openmlpimp.sklearn.beam_search.BeamSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,varience…
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol…
- sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold…
- sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
- weka.AdaBoostM1_J48(2)
- weka.AdaBoostM1_J48(3)
- weka.AdaBoostM1_LMT(3)
- weka.AttributeSelectedClassifier_AdaBoostM1_RandomForest(1)
- weka.Bagging_J48(2)
- weka.Bagging_LMT(2)
- weka.Bagging_LMT(3)
- weka.Decorate(1)
- weka.Decorate(2)
- weka.FilteredClassifier_AdaBoostM1_J48(1)
- weka.FilteredClassifier_AdaBoostM1_LMT(1)
- weka.FilteredClassifier_Bagging_LMT(1)
- weka.FilteredClassifier_MultiBoostAB_J48(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.FilteredClassifier_RandomForest(4)
- weka.MultiBoostAB_J48(3)
- weka.RandomCommittee_RandomTree(2)
- weka.RandomCommittee_RandomTree(4)
- weka.RandomForest(12)
- weka.RandomForest(9)
Published by OpenML.
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