OpenML-CC18: ozone-level-8hr
Best predictive accuracy per machine-learning flow on the ozone-level-8hr classification task from the OpenML-CC18 suite.
Community results for the ozone-level-8hr classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9978). 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 32 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| mlr.classif.xgboost(9) | 0.9503 | 0.9503 | 0.9503 | 0.9503 | 0.9503 | 0.9503 | 1 |
| mlr.classif.ksvm.preproc(2) | 0.9495 | 0.9495 | 0.9495 | 0.9495 | 0.9495 | 0.9495 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 1 |
| mlr.classif.svm(6) | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 1 |
| mlr.classif.svm(7) | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 0.9491 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9487 | 0.9487 | 0.9487 | 0.9487 | 0.9487 | 0.9487 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 0.9483 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(16) | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 1 |
| mlr.classif.svm.preproc(4) | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 1 |
| mlr.classif.ksvm.preproc(3) | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 0.9479 | 1 |
| mlr.classif.xgboost(6) | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 0.9471 | 1 |
| mlr.classif.ksvm.imputed.preproc(1) | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 1 |
| mlr.classif.ranger(13) | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 1 |
| classif.rotationForest(5) | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 0.9467 | 1 |
| mlr.classif.RRF.imputed.dummied.preproc(1) | 0.9463 | 0.9463 | 0.9463 | 0.9463 | 0.9463 | 0.9463 | 1 |
…and 7 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 (32)
- classif.rotationForest(5)
- mlr.classif.ksvm.imputed.preproc(1)
- mlr.classif.ksvm.preproc(2)
- mlr.classif.ksvm.preproc(3)
- mlr.classif.ranger(13)
- mlr.classif.RRF.imputed.dummied.preproc(1)
- mlr.classif.svm(6)
- mlr.classif.svm(7)
- mlr.classif.svm.preproc(4)
- mlr.classif.xgboost(4)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(9)
- sklearn.ensemble.forest.RandomForestClassifier(16)
- 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=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…
- 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(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=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=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
- 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)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
Published by OpenML.
No observations in this window.