OpenML-CC18: cylinder-bands
Best predictive accuracy per machine-learning flow on the cylinder-bands classification task from the OpenML-CC18 suite.
Community results for the cylinder-bands classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/14954). 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 19 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) | 0.8593 | 0.8593 | 0.8593 | 0.8593 | 0.8593 | 0.8593 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.8537 | 0.8537 | 0.8537 | 0.8537 | 0.8537 | 0.8537 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 0.8519 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 0.8481 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8463 | 0.8463 | 0.8463 | 0.8463 | 0.8463 | 0.8463 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8444 | 0.8444 | 0.8444 | 0.8444 | 0.8444 | 0.8444 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.8352 | 0.8352 | 0.8352 | 0.8352 | 0.8352 | 0.8352 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.8315 | 0.8315 | 0.8315 | 0.8315 | 0.8315 | 0.8315 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.8296 | 0.8296 | 0.8296 | 0.8296 | 0.8296 | 0.8296 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.8259 | 0.8259 | 0.8259 | 0.8259 | 0.8259 | 0.8259 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.8241 | 0.8241 | 0.8241 | 0.8241 | 0.8241 | 0.8241 | 1 |
| weka.kf.AdaBoostM1-SMO(1) | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 0.8185 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.8167 | 0.8167 | 0.8167 | 0.8167 | 0.8167 | 0.8167 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.8148 | 0.8148 | 0.8148 | 0.8148 | 0.8148 | 0.8148 | 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 (19)
- 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=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…
- 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=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=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
- 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,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn…
- 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,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)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.kf.AdaBoostM1-SMO(1)
Published by OpenML.
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