OpenML-CC18: optdigits
Best predictive accuracy per machine-learning flow on the optdigits classification task from the OpenML-CC18 suite.
Community results for the optdigits classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/28). 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 23 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… | 0.9938 | 0.9938 | 0.9938 | 0.9938 | 0.9938 | 0.9938 | 1 |
| sklearn.svm.classes.SVC(32) | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 0.9936 | 1 |
| sklearn.svm.classes.SVC(5) | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 0.9934 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9932 | 0.9932 | 0.9932 | 0.9932 | 0.9932 | 0.9932 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 0.9931 | 1 |
| sklearn.svm.classes.SVC(35) | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 0.9929 | 1 |
| weka.SMO_RBFKernel(1) | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 0.9923 | 1 |
| weka.SMO_RBFKernel(8) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| weka.SMO_RBFKernel(4) | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 0.9918 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 0.9907 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 0.9906 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 0.9904 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 1 |
| classif.rda(7) | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 1 |
| weka.SMO_PolyKernel(1) | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 1 |
| mlr.classif.rda.preproc(2) | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 0.9891 | 1 |
| weka.FilteredClassifier_SMO_PolyKernel(1) | 0.989 | 0.989 | 0.989 | 0.989 | 0.989 | 0.989 | 1 |
| weka.SMO_PolyKernel(15) | 0.989 | 0.989 | 0.989 | 0.989 | 0.989 | 0.989 | 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 (23)
- classif.rda(7)
- mlr.classif.rda.preproc(2)
- 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,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.svm.classes.SVC(32)
- sklearn.svm.classes.SVC(35)
- sklearn.svm.classes.SVC(5)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.FilteredClassifier_SMO_PolyKernel(1)
- weka.SMO_PolyKernel(1)
- weka.SMO_PolyKernel(15)
- weka.SMO_RBFKernel(1)
- weka.SMO_RBFKernel(4)
- weka.SMO_RBFKernel(8)
Published by OpenML.
No observations in this window.