OpenML-CC18: balance-scale
Best predictive accuracy per machine-learning flow on the balance-scale classification task from the OpenML-CC18 suite.
Community results for the balance-scale classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/11). 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 32 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.svm.classes.SVC(32) | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| weka.AttributeSelectedClassifier_AttributeSelectedClassifier_SMO_PolyKernel(1) | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| weka.AttributeSelectedClassifier_SMO_PolyKernel(2) | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 1 |
| mlr.classif.svm.preproc.preproc.tuned(4) | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 1 |
| weka.SMO_PolyKernel(11) | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 0.9984 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 1 |
| sklearn.neural_network.multilayer_perceptron.MLPClassifier(3) | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 1 |
| sklearn.svm.classes.SVC(5) | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 0.9952 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC)(1) | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| weka.SMO_PolyKernel(1) | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(4) | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,svc=sklearn.svm.classes.SVC)(1) | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 1 |
…and 7 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 (32)
- arbok.autosklearn.AutoSklearnWrapper(1)
- arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(1)
- arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)
- arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(4)
- mlr.classif.svm.preproc.preproc.tuned(4)
- 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=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.neural_network.multilayer_perceptron.MLPClassifier(3)
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
- sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(DualImputer=helper.dual_imputer.DualImputer,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,svc=sklearn.svm.classes.SVC)(1)
- 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,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(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC)(1)
- sklearn.svm.classes.SVC(32)
- sklearn.svm.classes.SVC(5)
- weka.AttributeSelectedClassifier_AttributeSelectedClassifier_SMO_PolyKernel(1)
- weka.AttributeSelectedClassifier_SMO_PolyKernel(2)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.FilteredClassifier_MultiSearch_MultilayerPerceptron(1)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.SMO_PolyKernel(1)
- weka.SMO_PolyKernel(11)
- weka.SMO_RBFKernel(1)
Published by OpenML.
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