OpenML-CC18: eucalyptus
Best predictive accuracy per machine-learning flow on the eucalyptus classification task from the OpenML-CC18 suite.
Community results for the eucalyptus classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/2079). 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 48 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… | 0.6997 | 0.6997 | 0.6997 | 0.6997 | 0.6997 | 0.6997 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.6943 | 0.6943 | 0.6943 | 0.6943 | 0.6943 | 0.6943 | 1 |
| weka.RotationForest_PrincipalComponents_J48(14) | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 1 |
| weka.RotationForest_PrincipalComponents_J48(3) | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 1 |
| weka.Bagging_LMT(2) | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 1 |
| weka.Bagging_BFTree(1) | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 0.6916 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 0.6902 | 1 |
| weka.AdaBoostM1_BFTree(2) | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 0.6889 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 1 |
| weka.MultiBoostAB_BFTree(1) | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 0.6875 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(1) | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2) | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| classif.boosting(12) | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 0.6861 | 1 |
| weka.AdaBoostM1_JRip(2) | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 1 |
| weka.AdaBoostM1_JRip(1) | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 0.6848 | 1 |
| weka.AdaBoostM1_PART(1) | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 1 |
| weka.AdaBoostM1_LMT(2) | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn… | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 0.6834 | 1 |
…and 23 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 (48)
- classif.boosting(12)
- classif.randomForest(57)
- classif.randomForestSRC(10)
- classif.randomForestSRCSyn(2)
- classif.ranger(8)
- openmlpimp.sklearn.beam_search.BeamSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,varience…
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- 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(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(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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(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(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
- sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
- sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=xgboost.sklearn.XGBClassifier)(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,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,svc=sklearn.svm.classes.SVC)(1)
- weka.AdaBoostM1_BFTree(2)
- weka.AdaBoostM1_JRip(1)
- weka.AdaBoostM1_JRip(2)
- weka.AdaBoostM1_LMT(1)
- weka.AdaBoostM1_LMT(2)
- weka.AdaBoostM1_PART(1)
- weka.AttributeSelectedClassifier_SimpleLogistic(2)
- weka.Bagging_BFTree(1)
- weka.Bagging_JRip(1)
- weka.Bagging_JRip(2)
- weka.Bagging_LMT(1)
- weka.Bagging_LMT(2)
- weka.FilteredClassifier_AttributeSelectedClassifier_Bagging_JRip(1)
- weka.MultiBoostAB_BFTree(1)
- weka.MultiBoostAB_LMT(1)
- weka.RotationForest_PrincipalComponents_J48(14)
- weka.RotationForest_PrincipalComponents_J48(3)
- weka.SimpleLogistic(6)
Published by OpenML.
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