OpenML-CC18: pendigits
Best predictive accuracy per machine-learning flow on the pendigits classification task from the OpenML-CC18 suite.
Community results for the pendigits classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/32). 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 18 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.997 | 0.997 | 0.997 | 0.997 | 0.997 | 0.997 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 0.9968 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 0.9966 | 1 |
| sklearn.svm.classes.SVC(32) | 0.9965 | 0.9965 | 0.9965 | 0.9965 | 0.9965 | 0.9965 | 1 |
| weka.SMO_RBFKernel(1) | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4) | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 0.9964 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 0.9963 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.9962 | 0.9962 | 0.9962 | 0.9962 | 0.9962 | 0.9962 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9961 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | 1 |
| mlr.classif.svm.preproc.preproc.tuned(18) | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(2) | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco… | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 0.9958 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 0.9957 | 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 (18)
- mlr.classif.svm.preproc.preproc.tuned(18)
- 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(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,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)(4)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(2)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.svm.classes.SVC(32)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.SMO_RBFKernel(1)
Published by OpenML.
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