smplmark

OpenML-CC18: breast-w

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

Community results for the breast-w classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/15). 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 (50)

  • classif.randomForestSRC(10)
  • classif.ranger(8)
  • mlr.classif.earth.imputed.preproc(1)
  • mlr.classif.randomForestSRC(2)
  • mlr.classif.ranger.imputed.dummied.preproc(1)
  • 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(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
  • sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)
  • sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
  • sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,neuralnetwork=helper.nn.NeuralNetwork)(1)
  • 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(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold…
  • sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.Varia…
  • sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2)
  • 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,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
  • weka.A1DE(2)
  • weka.A1DE(28)
  • weka.A1DE(4)
  • weka.AttributeSelectedClassifier_A1DE(1)
  • weka.AttributeSelectedClassifier_Bagging_BayesNet(1)
  • weka.AttributeSelectedClassifier_NaiveBayes(2)
  • weka.AttributeSelectedClassifier_PrincipalComponents_Ranker_J48(1)
  • weka.Bagging_RandomForest(2)
  • weka.Bagging_RandomForest(9)
  • weka.FilteredClassifier_A1DE(1)
  • weka.FilteredClassifier_AttributeSelectedClassifier_A1DE(1)
  • weka.FilteredClassifier_AttributeSelectedClassifier_IBk(1)
  • weka.FilteredClassifier_IBk(1)
  • weka.FilteredClassifier_MultiSearch_RandomForest(1)
  • weka.FilteredClassifier_PrincipalComponents_J48(1)
  • weka.IBk(1)
  • weka.IBk(4)
  • weka.kf.AttributeSelection-BestFirst-CfsSubsetEval-Standardize-IBk5(1)
  • weka.kf.AttributeSelection-Ranker-InfoGain-Standardize-IBk5(1)
  • weka.kf.AttributeSelection-Ranker-ReliefF-Standardize-IBk5(1)
  • weka.kf.ReplaceMissingValues-PKIDiscretize-NaiveBayes(1)
  • weka.NaiveBayes(13)
  • weka.RotationForest_PrincipalComponents_J48(14)
  • weka.RotationForest_PrincipalComponents_J48(3)

Published by OpenML.