OpenML-CC18: sick
Best predictive accuracy per machine-learning flow on the sick classification task from the OpenML-CC18 suite.
Community results for the sick classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3021). 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 45 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… | 0.9928 | 0.9928 | 0.9928 | 0.9928 | 0.9928 | 0.9928 | 1 |
| classif.boosting(12) | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.992 | 0.992 | 0.992 | 0.992 | 0.992 | 0.992 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.992 | 0.992 | 0.992 | 0.992 | 0.992 | 0.992 | 1 |
| weka.AdaBoostM1_LMT(2) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| weka.Bagging_LMT(2) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9913 | 0.9913 | 0.9913 | 0.9913 | 0.9913 | 0.9913 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| weka.kf.AdaBoostM1-J48(1) | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| weka.AdaBoostM1_J48(3) | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| weka.AdaBoostM1_LADTree(2) | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| weka.AdaBoostM1_J48(2) | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 1 |
| weka.MultiBoostAB_ADTree(2) | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 1 |
| weka.AdaBoostM1_LMT(3) | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 1 |
| classif.randomForestSRCSyn(2) | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 1 |
…and 20 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 (45)
- arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklear…
- classif.boosting(12)
- classif.J48(28)
- classif.randomForestSRCSyn(2)
- mlr.classif.C50.preproc(29)
- mlr.classif.xgboost.imputed.dummied.preproc(1)
- 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.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.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=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…
- 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(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=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
- weka.AdaBoostM1_BFTree(2)
- weka.AdaBoostM1_J48(2)
- weka.AdaBoostM1_J48(3)
- weka.AdaBoostM1_LADTree(2)
- weka.AdaBoostM1_LMT(2)
- weka.AdaBoostM1_LMT(3)
- weka.AdaBoostM1_REPTree(2)
- weka.Bagging_J48(2)
- weka.Bagging_J48(5)
- weka.Bagging_LMT(2)
- weka.Bagging_LMT(3)
- weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.kf.AdaBoostM1-J48(1)
- weka.kf.Bagging-J48(1)
- weka.kf.ReplaceMissingValues-J48(1)
- weka.LMT(3)
- weka.LMT(4)
- weka.LWL_J48(3)
- weka.MultiBoostAB_ADTree(2)
- weka.RandomForest(9)
- weka.RotationForest_PrincipalComponents_J48(14)
Published by OpenML.
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