OpenML-CC18: mfeat-karhunen
Best predictive accuracy per machine-learning flow on the mfeat-karhunen classification task from the OpenML-CC18 suite.
Community results for the mfeat-karhunen classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/16). 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 12 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… | 0.9855 | 0.9855 | 0.9855 | 0.9855 | 0.9855 | 0.9855 | 1 |
| sklearn.svm._classes.SVC(18) | 0.985 | 0.985 | 0.985 | 0.985 | 0.985 | 0.985 | 1 |
| sklearn.svm.classes.SVC(5) | 0.9845 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | 1 |
| sklearn.svm.classes.SVC(32) | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 1 |
| sklearn.svm.classes.SVC(35) | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2) | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| sklearn.svm.classes.SVC(31) | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| classif.rda(7) | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 1 |
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 (12)
- arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)
- classif.rda(7)
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
- 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,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,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.svm._classes.SVC(18)
- sklearn.svm.classes.SVC(31)
- sklearn.svm.classes.SVC(32)
- sklearn.svm.classes.SVC(35)
- sklearn.svm.classes.SVC(5)
Published by OpenML.
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