OpenML-CC18: adult
Best predictive accuracy per machine-learning flow on the adult classification task from the OpenML-CC18 suite.
Community results for the adult classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/7592). 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 24 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8756 | 0.8756 | 0.8756 | 0.8756 | 0.8756 | 0.8756 | 1 |
| automlbenchmark_tpot(1) | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 1 |
| automlbenchmark_h2oautoml(1) | 0.8743 | 0.8743 | 0.8743 | 0.8743 | 0.8743 | 0.8743 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2) | 0.8739 | 0.8739 | 0.8739 | 0.8739 | 0.8739 | 0.8739 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.8738 | 0.8738 | 0.8738 | 0.8738 | 0.8738 | 0.8738 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.873 | 0.873 | 0.873 | 0.873 | 0.873 | 0.873 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.873 | 0.873 | 0.873 | 0.873 | 0.873 | 0.873 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.8728 | 0.8728 | 0.8728 | 0.8728 | 0.8728 | 0.8728 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.8723 | 0.8723 | 0.8723 | 0.8723 | 0.8723 | 0.8723 | 1 |
| mlr.classif.C50.preproc(54) | 0.8707 | 0.8707 | 0.8707 | 0.8707 | 0.8707 | 0.8707 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(… | 0.868 | 0.868 | 0.868 | 0.868 | 0.868 | 0.868 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8675 | 0.8675 | 0.8675 | 0.8675 | 0.8675 | 0.8675 | 1 |
| weka.FilteredClassifier_J48(1) | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.867 | 0.867 | 0.867 | 0.867 | 0.867 | 0.867 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.8669 | 0.8669 | 0.8669 | 0.8669 | 0.8669 | 0.8669 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8668 | 0.8668 | 0.8668 | 0.8668 | 0.8668 | 0.8668 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8664 | 0.8664 | 0.8664 | 0.8664 | 0.8664 | 0.8664 | 1 |
| weka.FilteredClassifier_MultiSearch_LogitBoost_REPTree(1) | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8661 | 0.8661 | 0.8661 | 0.8661 | 0.8661 | 0.8661 | 1 |
| classif.ranger(8) | 0.8656 | 0.8656 | 0.8656 | 0.8656 | 0.8656 | 0.8656 | 1 |
| automlbenchmark_autosklearn(1) | 0.8654 | 0.8654 | 0.8654 | 0.8654 | 0.8654 | 0.8654 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.865 | 0.865 | 0.865 | 0.865 | 0.865 | 0.865 | 1 |
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 (24)
- automlbenchmark_autosklearn(1)
- automlbenchmark_h2oautoml(1)
- automlbenchmark_tpot(1)
- classif.ranger(8)
- mlr.classif.C50.preproc(54)
- 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(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot…
- 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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2)
- 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(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
- weka.FilteredClassifier_J48(1)
- weka.FilteredClassifier_MultiSearch_LogitBoost_REPTree(1)
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