OpenML-CC18: wdbc

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

Community results for the wdbc classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9946). 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 16 measurements, ranked by average.

SubjectAvgMedianMinMaxP95P99Count
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)0.98420.98420.98420.98420.98420.98421
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…0.98420.98420.98420.98420.98420.98421
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.98240.98240.98240.98240.98240.98241
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)0.98240.98240.98240.98240.98240.98241
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…0.98240.98240.98240.98240.98240.98241
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…0.98240.98240.98240.98240.98240.98241
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…0.98240.98240.98240.98240.98240.98241
mlr.classif.svm(6)0.98240.98240.98240.98240.98240.98241
mlr.classif.glmnet(11)0.98240.98240.98240.98240.98240.98241
mlr.classif.glmnet(5)0.98240.98240.98240.98240.98240.98241
mlr.classif.svm(7)0.98240.98240.98240.98240.98240.98241
weka.MultiClassClassifierUpdateable_SGD(3)0.98240.98240.98240.98240.98240.98241
weka.SGD(4)0.98240.98240.98240.98240.98240.98241
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)0.98070.98070.98070.98070.98070.98071
weka.KernelLogisticRegression_RBFKernel(3)0.98070.98070.98070.98070.98070.98071
classif.ksvm(4)0.98070.98070.98070.98070.98070.98071

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 (16)

  • classif.ksvm(4)
  • mlr.classif.glmnet(11)
  • mlr.classif.glmnet(5)
  • mlr.classif.svm(6)
  • mlr.classif.svm(7)
  • sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)
  • 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,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…
  • sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…
  • sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
  • 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,svc=sklearn.svm.classes.SVC)(1)
  • weka.KernelLogisticRegression_RBFKernel(3)
  • weka.MultiClassClassifierUpdateable_SGD(3)
  • weka.SGD(4)

Published by OpenML.