OpenML-CC18: nomao
Best predictive accuracy per machine-learning flow on the nomao classification task from the OpenML-CC18 suite.
Community results for the nomao classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9977). 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 30 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) | 0.9761 | 0.9761 | 0.9761 | 0.9761 | 0.9761 | 0.9761 | 1 |
| mlr.classif.xgboost(6) | 0.9755 | 0.9755 | 0.9755 | 0.9755 | 0.9755 | 0.9755 | 1 |
| mlr.classif.xgboost(9) | 0.9754 | 0.9754 | 0.9754 | 0.9754 | 0.9754 | 0.9754 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 1 |
| mlr.classif.xgboost(4) | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 0.9748 | 1 |
| automlbenchmark_h2oautoml(1) | 0.973 | 0.973 | 0.973 | 0.973 | 0.973 | 0.973 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 1 |
| automlbenchmark_autosklearn(1) | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 1 |
| automlbenchmark_tpot(1) | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 1 |
| mlr.classif.ranger(15) | 0.9707 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | 1 |
| mlr.classif.ranger(9) | 0.9706 | 0.9706 | 0.9706 | 0.9706 | 0.9706 | 0.9706 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.9704 | 0.9704 | 0.9704 | 0.9704 | 0.9704 | 0.9704 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9703 | 0.9703 | 0.9703 | 0.9703 | 0.9703 | 0.9703 | 1 |
| mlr.classif.ranger(16) | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| mlr.classif.ranger(13) | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 1 |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 1 |
| automlbenchmark_tunedrandomforest(1) | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 1 |
| weka.RandomForest(9) | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 0.9698 | 1 |
| automlbenchmark_randomforest(1) | 0.9697 | 0.9697 | 0.9697 | 0.9697 | 0.9697 | 0.9697 | 1 |
…and 5 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 (30)
- automlbenchmark_autosklearn(1)
- automlbenchmark_h2oautoml(1)
- automlbenchmark_randomforest(1)
- automlbenchmark_tpot(1)
- automlbenchmark_tunedrandomforest(1)
- classif.randomForest(57)
- classif.randomForestSRC(10)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.xgboost(4)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(9)
- sklearn.ensemble.forest.ExtraTreesClassifier(5)
- 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_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(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(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- 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.classifiers.trees.RandomForest(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.RandomForest(9)
Published by OpenML.
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