smplmark

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 target 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.

Metrics

  • predictive_accuracy

Targets (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.