OpenML-CC18: first-order-theorem-proving
Best predictive accuracy per machine-learning flow on the first-order-theorem-proving classification task from the OpenML-CC18 suite.
Community results for the first-order-theorem-proving classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9985). 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 30 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 0.6389 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.6381 | 0.6381 | 0.6381 | 0.6381 | 0.6381 | 0.6381 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.638 | 0.638 | 0.638 | 0.638 | 0.638 | 0.638 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.637 | 0.637 | 0.637 | 0.637 | 0.637 | 0.637 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.6365 | 0.6365 | 0.6365 | 0.6365 | 0.6365 | 0.6365 | 1 |
| weka.RandomForest(9) | 0.6363 | 0.6363 | 0.6363 | 0.6363 | 0.6363 | 0.6363 | 1 |
| mlr.classif.RRF.preproc(2) | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 1 |
| classif.randomForest(50) | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 1 |
| sklearn.ensemble.forest.ExtraTreesClassifier(5) | 0.6335 | 0.6335 | 0.6335 | 0.6335 | 0.6335 | 0.6335 | 1 |
| classif.randomForest(43) | 0.6334 | 0.6334 | 0.6334 | 0.6334 | 0.6334 | 0.6334 | 1 |
| classif.randomForestSRC(7) | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 1 |
| classif.ranger(5) | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 0.6329 | 1 |
| mlr.classif.randomForestSRC.preproc(2) | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 0.6327 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.6324 | 0.6324 | 0.6324 | 0.6324 | 0.6324 | 0.6324 | 1 |
| mlr.classif.ranger.preproc.preproc.tuned(65) | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 1 |
| arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklear… | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 0.6322 | 1 |
| mlr.classif.randomForest.preproc(5) | 0.6321 | 0.6321 | 0.6321 | 0.6321 | 0.6321 | 0.6321 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.6314 | 0.6314 | 0.6314 | 0.6314 | 0.6314 | 0.6314 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(16) | 0.6304 | 0.6304 | 0.6304 | 0.6304 | 0.6304 | 0.6304 | 1 |
| weka.kf.RandomForest(1) | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 1 |
| weka.classifiers.trees.RandomForest(1) | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 0.6296 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.6288 | 0.6288 | 0.6288 | 0.6288 | 0.6288 | 0.6288 | 1 |
| weka.Bagging_RandomForest(9) | 0.6247 | 0.6247 | 0.6247 | 0.6247 | 0.6247 | 0.6247 | 1 |
…and 5 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 (30)
- arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklear…
- classif.randomForest(43)
- classif.randomForest(50)
- classif.randomForestSRC(7)
- classif.ranger(5)
- mlr.classif.randomForest.preproc(5)
- mlr.classif.randomForestSRC.preproc(2)
- mlr.classif.ranger.preproc.preproc.tuned(65)
- mlr.classif.RRF.preproc(2)
- 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=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.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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