OpenML-CC18: GesturePhaseSegmentationProcessed
Best predictive accuracy per machine-learning flow on the GesturePhaseSegmentationProcessed classification task from the OpenML-CC18 suite.
Community results for the GesturePhaseSegmentationProcessed classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/14969). 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 27 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) | 0.7347 | 0.7347 | 0.7347 | 0.7347 | 0.7347 | 0.7347 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.7211 | 0.7211 | 0.7211 | 0.7211 | 0.7211 | 0.7211 | 1 |
| sklearn.ensemble.forest.ExtraTreesClassifier(5) | 0.7121 | 0.7121 | 0.7121 | 0.7121 | 0.7121 | 0.7121 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.7114 | 0.7114 | 0.7114 | 0.7114 | 0.7114 | 0.7114 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.7102 | 0.7102 | 0.7102 | 0.7102 | 0.7102 | 0.7102 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(16) | 0.7101 | 0.7101 | 0.7101 | 0.7101 | 0.7101 | 0.7101 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.7094 | 0.7094 | 0.7094 | 0.7094 | 0.7094 | 0.7094 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.708 | 0.708 | 0.708 | 0.708 | 0.708 | 0.708 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.7056 | 0.7056 | 0.7056 | 0.7056 | 0.7056 | 0.7056 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.7029 | 0.7029 | 0.7029 | 0.7029 | 0.7029 | 0.7029 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.699 | 0.699 | 0.699 | 0.699 | 0.699 | 0.699 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.699 | 0.699 | 0.699 | 0.699 | 0.699 | 0.699 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.6987 | 0.6987 | 0.6987 | 0.6987 | 0.6987 | 0.6987 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.6971 | 0.6971 | 0.6971 | 0.6971 | 0.6971 | 0.6971 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.6946 | 0.6946 | 0.6946 | 0.6946 | 0.6946 | 0.6946 | 1 |
| weka.RandomForest(9) | 0.6881 | 0.6881 | 0.6881 | 0.6881 | 0.6881 | 0.6881 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 0.6857 | 1 |
| classif.randomForest(57) | 0.6851 | 0.6851 | 0.6851 | 0.6851 | 0.6851 | 0.6851 | 1 |
| weka.kf.RandomForest(1) | 0.6849 | 0.6849 | 0.6849 | 0.6849 | 0.6849 | 0.6849 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.6833 | 0.6833 | 0.6833 | 0.6833 | 0.6833 | 0.6833 | 1 |
| mlr.classif.ranger.preproc.preproc.tuned(69) | 0.6794 | 0.6794 | 0.6794 | 0.6794 | 0.6794 | 0.6794 | 1 |
| weka.classifiers.trees.RandomForest(1) | 0.6791 | 0.6791 | 0.6791 | 0.6791 | 0.6791 | 0.6791 | 1 |
| classif.randomForestSRC(10) | 0.6788 | 0.6788 | 0.6788 | 0.6788 | 0.6788 | 0.6788 | 1 |
| classif.ranger(8) | 0.6755 | 0.6755 | 0.6755 | 0.6755 | 0.6755 | 0.6755 | 1 |
…and 2 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 (27)
- classif.randomForest(57)
- classif.randomForestSRC(10)
- classif.ranger(8)
- mlr.classif.ranger.preproc.preproc.tuned(69)
- sklearn.ensemble.forest.ExtraTreesClassifier(5)
- sklearn.ensemble.forest.RandomForestClassifier(16)
- 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=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=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)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- weka.AttributeSelectedClassifier_RandomForest(1)
- weka.Bagging_RandomForest(9)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.classifiers.trees.RandomForest(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.kf.RandomForest(1)
- weka.RandomForest(9)
Published by OpenML.
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