smplmark

OpenML-CC18: sick

Best predictive accuracy per machine-learning flow on the sick classification task from the OpenML-CC18 suite.

Community results for the sick classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3021). 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 (45)

  • arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklear…
  • classif.boosting(12)
  • classif.J48(28)
  • classif.randomForestSRCSyn(2)
  • mlr.classif.C50.preproc(29)
  • mlr.classif.xgboost.imputed.dummied.preproc(1)
  • 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=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…
  • 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.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=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…
  • sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
  • sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
  • sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
  • 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.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol…
  • sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho…
  • 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(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
  • sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
  • weka.AdaBoostM1_BFTree(2)
  • weka.AdaBoostM1_J48(2)
  • weka.AdaBoostM1_J48(3)
  • weka.AdaBoostM1_LADTree(2)
  • weka.AdaBoostM1_LMT(2)
  • weka.AdaBoostM1_LMT(3)
  • weka.AdaBoostM1_REPTree(2)
  • weka.Bagging_J48(2)
  • weka.Bagging_J48(5)
  • weka.Bagging_LMT(2)
  • weka.Bagging_LMT(3)
  • weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3)
  • weka.FilteredClassifier_MultiSearch_RandomForest(1)
  • weka.kf.AdaBoostM1-J48(1)
  • weka.kf.Bagging-J48(1)
  • weka.kf.ReplaceMissingValues-J48(1)
  • weka.LMT(3)
  • weka.LMT(4)
  • weka.LWL_J48(3)
  • weka.MultiBoostAB_ADTree(2)
  • weka.RandomForest(9)
  • weka.RotationForest_PrincipalComponents_J48(14)

Published by OpenML.