smplmark

OpenML-CC18: jm1

Best predictive accuracy per machine-learning flow on the jm1 classification task from the OpenML-CC18 suite.

Community results for the jm1 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3904). 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 (28)

  • classif.randomForest(57)
  • classif.randomForestSRC(10)
  • classif.randomForestSRCSyn(2)
  • classif.ranger(8)
  • mlr.classif.ranger.imputed.dummied.preproc(1)
  • mlr.classif.RRF.imputed.dummied.preproc(1)
  • 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=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…
  • sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…
  • 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.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(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
  • sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
  • sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)
  • sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
  • weka.AttributeSelectedClassifier_GainRatioAttributeEval_Ranker_RandomForest(1)
  • weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2)
  • weka.Bagging_J48(2)
  • weka.Bagging_LMT(2)
  • weka.Bagging_RandomForest(9)
  • weka.classifiers.trees.RandomForest(1)
  • weka.FilteredClassifier_MultiSearch_RandomForest(1)
  • weka.kf.RandomForest(1)
  • weka.RandomForest(5)
  • weka.RandomForest(9)

Published by OpenML.