OpenML-CC18: jm1
Best predictive accuracy per machine-learning flow on the jm1 classification task from the OpenML-CC18 suite.
Community results for the jm1 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3904). 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 28 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| classif.ranger(8) | 0.825 | 0.825 | 0.825 | 0.825 | 0.825 | 0.825 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 0.8243 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.824 | 0.824 | 0.824 | 0.824 | 0.824 | 0.824 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8239 | 0.8239 | 0.8239 | 0.8239 | 0.8239 | 0.8239 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.8238 | 0.8238 | 0.8238 | 0.8238 | 0.8238 | 0.8238 | 1 |
| classif.randomForest(57) | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 1 |
| weka.Bagging_LMT(2) | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 0.8237 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8235 | 0.8235 | 0.8235 | 0.8235 | 0.8235 | 0.8235 | 1 |
| mlr.classif.ranger.imputed.dummied.preproc(1) | 0.8234 | 0.8234 | 0.8234 | 0.8234 | 0.8234 | 0.8234 | 1 |
| weka.Bagging_J48(2) | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 0.8226 | 1 |
| classif.randomForestSRC(10) | 0.822 | 0.822 | 0.822 | 0.822 | 0.822 | 0.822 | 1 |
| weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2) | 0.8206 | 0.8206 | 0.8206 | 0.8206 | 0.8206 | 0.8206 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 0.8205 | 1 |
| weka.AttributeSelectedClassifier_GainRatioAttributeEval_Ranker_RandomForest(1) | 0.82 | 0.82 | 0.82 | 0.82 | 0.82 | 0.82 | 1 |
| weka.Bagging_RandomForest(9) | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 0.8199 | 1 |
| classif.randomForestSRCSyn(2) | 0.8198 | 0.8198 | 0.8198 | 0.8198 | 0.8198 | 0.8198 | 1 |
| mlr.classif.RRF.imputed.dummied.preproc(1) | 0.8192 | 0.8192 | 0.8192 | 0.8192 | 0.8192 | 0.8192 | 1 |
| weka.kf.RandomForest(1) | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 1 |
| weka.RandomForest(9) | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 1 |
| weka.RandomForest(5) | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 0.8191 | 1 |
…and 3 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 (28)
- classif.randomForest(57)
- classif.randomForestSRC(10)
- classif.randomForestSRCSyn(2)
- classif.ranger(8)
- mlr.classif.ranger.imputed.dummied.preproc(1)
- mlr.classif.RRF.imputed.dummied.preproc(1)
- 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.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(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(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- 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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- weka.AttributeSelectedClassifier_GainRatioAttributeEval_Ranker_RandomForest(1)
- weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2)
- weka.Bagging_J48(2)
- weka.Bagging_LMT(2)
- weka.Bagging_RandomForest(9)
- weka.classifiers.trees.RandomForest(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.kf.RandomForest(1)
- weka.RandomForest(5)
- weka.RandomForest(9)
Published by OpenML.
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