OpenML-CC18: splice
Best predictive accuracy per machine-learning flow on the splice classification task from the OpenML-CC18 suite.
Community results for the splice classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/45). 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 37 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| weka.MultilayerPerceptron(8) | 0.9881 | 0.9881 | 0.9881 | 0.9881 | 0.9881 | 0.9881 | 1 |
| weka.RandomForest(9) | 0.9715 | 0.9715 | 0.9715 | 0.9715 | 0.9715 | 0.9715 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(4) | 0.9705 | 0.9705 | 0.9705 | 0.9705 | 0.9705 | 0.9705 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| classif.randomForestSRC(7) | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| classif.randomForest(43) | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 0.9702 | 1 |
| mlr.classif.randomForestSRC.preproc(2) | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1) | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 0.9699 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9696 | 0.9696 | 0.9696 | 0.9696 | 0.9696 | 0.9696 | 1 |
| mlr.classif.randomForest.preproc(5) | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 0.9693 | 1 |
| classif.cforest(9) | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| classif.cforest(6) | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClass… | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClass… | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(2) | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 1 |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1) | 0.9687 | 0.9687 | 0.9687 | 0.9687 | 0.9687 | 0.9687 | 1 |
| weka.SMO_PolyKernel(15) | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 1 |
| weka.A2DEUpdateable(1) | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 1 |
| weka.A2DE(5) | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 0.9683 | 1 |
…and 12 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 (37)
- classif.cforest(6)
- classif.cforest(9)
- classif.randomForest(43)
- classif.randomForestSRC(7)
- mlr.classif.cforest.preproc(2)
- mlr.classif.cforest.preproc(3)
- mlr.classif.randomForest.preproc(5)
- mlr.classif.randomForestSRC.preproc(2)
- 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=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…
- 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=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…
- 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(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClass…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClass…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(2)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(4)
- weka.A2DE(5)
- weka.A2DEUpdateable(1)
- weka.AttributeSelectedClassifier_SMO_PolyKernel(2)
- weka.Bagging_RandomForest(9)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.MultilayerPerceptron(8)
- weka.RandomForest(9)
- weka.SMO_PolyKernel(15)
Published by OpenML.
No observations in this window.