OpenML-CC18: har
Best predictive accuracy per machine-learning flow on the har classification task from the OpenML-CC18 suite.
Community results for the har classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/14970). 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 24 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… | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9956 | 0.9956 | 0.9956 | 0.9956 | 0.9956 | 0.9956 | 1 |
| sklearn.svm.classes.SVC(32) | 0.9948 | 0.9948 | 0.9948 | 0.9948 | 0.9948 | 0.9948 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9946 | 0.9946 | 0.9946 | 0.9946 | 0.9946 | 0.9946 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.9939 | 0.9939 | 0.9939 | 0.9939 | 0.9939 | 0.9939 | 1 |
| classif.boosting(12) | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9935 | 0.9935 | 0.9935 | 0.9935 | 0.9935 | 0.9935 | 1 |
| sklearn.svm.classes.SVC(5) | 0.9933 | 0.9933 | 0.9933 | 0.9933 | 0.9933 | 0.9933 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 1 |
| sklearn.svm.classes.SVC(35) | 0.9921 | 0.9921 | 0.9921 | 0.9921 | 0.9921 | 0.9921 | 1 |
| sklearn.svm.classes.SVC(31) | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 0.9915 | 1 |
| weka.AdaBoostM1_LMT(3) | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 0.9905 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 0.9899 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4) | 0.9894 | 0.9894 | 0.9894 | 0.9894 | 0.9894 | 0.9894 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco… | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9888 | 0.9888 | 0.9888 | 0.9888 | 0.9888 | 0.9888 | 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 (24)
- classif.boosting(12)
- 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=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(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(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…
- 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,svc=sklearn.svm.classes.SVC)(4)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.svm.classes.SVC(31)
- sklearn.svm.classes.SVC(32)
- sklearn.svm.classes.SVC(35)
- sklearn.svm.classes.SVC(5)
- weka.AdaBoostM1_LMT(3)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
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