OpenML-CC18: spambase
Best predictive accuracy per machine-learning flow on the spambase classification task from the OpenML-CC18 suite.
Community results for the spambase classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/43). 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 52 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… | 0.9626 | 0.9626 | 0.9626 | 0.9626 | 0.9626 | 0.9626 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9598 | 0.9598 | 0.9598 | 0.9598 | 0.9598 | 0.9598 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9589 | 0.9589 | 0.9589 | 0.9589 | 0.9589 | 0.9589 | 1 |
| classif.extraTrees(5) | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 1 |
| weka.MultiBoostAB_LMT(1) | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 0.9587 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9583 | 0.9583 | 0.9583 | 0.9583 | 0.9583 | 0.9583 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.9581 | 0.9581 | 0.9581 | 0.9581 | 0.9581 | 0.9581 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.9576 | 0.9576 | 0.9576 | 0.9576 | 0.9576 | 0.9576 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 0.9574 | 1 |
| weka.MultiBoostAB_RandomForest(1) | 0.9572 | 0.9572 | 0.9572 | 0.9572 | 0.9572 | 0.9572 | 1 |
| weka-randomforest-9 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 1 |
| weka-rotationforest-principalcomponents-j48-14 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3) | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 0.957 | 1 |
| mlr.classif.xgboost(9) | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 0.9567 | 1 |
| mlr.classif.ranger(9) | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 1 |
| weka.RandomForest(12) | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 1 |
| mlr.classif.xgboost(6) | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 0.9565 | 1 |
| mlr.classif.ranger(13) | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 0.9563 | 1 |
| weka.AdaBoostM1_LMT(2) | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 1 |
| sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(2) | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | 1 |
…and 27 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 (52)
- classif.boosting(6)
- classif.extraTrees(5)
- classif.randomForestSRC(7)
- mlr.classif.gbm.preproc.preproc.tuned(7)
- mlr.classif.randomForest.preproc(5)
- mlr.classif.randomForestSRC.preproc(2)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(9)
- mlr.classif.ranger.preproc.preproc.tuned(17)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(9)
- randomforest(1)
- sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)
- sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(2)
- 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(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(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(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.AdaBoostM1_LADTree(1)
- weka.AdaBoostM1_LADTree(2)
- weka.AdaBoostM1_LMT(2)
- weka.AdaBoostM1_RandomForest(1)
- weka.AdaBoostM1_RandomForest(2)
- weka.AttributeSelectedClassifier_GainRatioAttributeEval_Ranker_RandomForest(1)
- weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2)
- weka.Bagging_RandomForest(1)
- weka.Bagging_RandomForest(2)
- weka.Bagging_RandomTree(1)
- 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.FilteredClassifier_RandomForest(4)
- weka.MultiBoostAB_LMT(1)
- weka.MultiBoostAB_RandomForest(1)
- weka.MultiBoostAB_RandomTree(1)
- weka.MultiBoostAB_REPTree(1)
- weka.RandomForest(1)
- weka.RandomForest(12)
- weka.RandomForest(2)
- weka.RandomForest(5)
…and 2 more.
Published by OpenML.
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