OpenML-CC18: pc4
Best predictive accuracy per machine-learning flow on the pc4 classification task from the OpenML-CC18 suite.
Community results for the pc4 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3902). 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 37 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9294 | 0.9294 | 0.9294 | 0.9294 | 0.9294 | 0.9294 | 1 |
| mlr.classif.xgboost(9) | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 0.9211 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.9204 | 0.9204 | 0.9204 | 0.9204 | 0.9204 | 0.9204 | 1 |
| weka.Bagging_LMT(2) | 0.9198 | 0.9198 | 0.9198 | 0.9198 | 0.9198 | 0.9198 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 1 |
| mlr.classif.xgboost(7) | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 1 |
| weka.RotationForest_PrincipalComponents_J48(14) | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 0.9191 | 1 |
| mlr.classif.xgboost(6) | 0.9184 | 0.9184 | 0.9184 | 0.9184 | 0.9184 | 0.9184 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 1 |
| mlr.classif.gbm.preproc.preproc.tuned(18) | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 0.9177 | 1 |
| mlr.classif.ranger(13) | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 1 |
| sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 1 |
| weka.AdaBoostM1_LMT(2) | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 0.917 | 1 |
| mlr.classif.svm(6) | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 1 |
| weka.RotationForest_PrincipalComponents_J48(3) | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 0.9163 | 1 |
| mlr.classif.ranger(16) | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 1 |
| mlr.classif.ranger(9) | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 1 |
| mlr.classif.svm(7) | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(… | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 0.9156 | 1 |
…and 12 more subjects.
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 (37)
- mlr.classif.gbm.preproc.preproc.tuned(18)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.svm(6)
- mlr.classif.svm(7)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(7)
- mlr.classif.xgboost(9)
- 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_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(DualImputer=helper.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregressio…
- 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,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,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
- sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(…
- sklearn.pipeline.Pipeline(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…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- weka.AdaBoostM1_LMT(2)
- weka.Bagging_J48(2)
- weka.Bagging_LMT(2)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.KernelLogisticRegression_RBFKernel(1)
- weka.KernelLogisticRegression_RBFKernel(3)
- weka.RotationForest_PrincipalComponents_J48(14)
- weka.RotationForest_PrincipalComponents_J48(3)
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