OpenML-CC18: mfeat-zernike
Best predictive accuracy per machine-learning flow on the mfeat-zernike classification task from the OpenML-CC18 suite.
Community results for the mfeat-zernike classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/22). 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 33 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… | 0.907 | 0.907 | 0.907 | 0.907 | 0.907 | 0.907 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.903 | 0.903 | 0.903 | 0.903 | 0.903 | 0.903 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.899 | 0.899 | 0.899 | 0.899 | 0.899 | 0.899 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.8975 | 0.8975 | 0.8975 | 0.8975 | 0.8975 | 0.8975 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4) | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 0.8745 | 1 |
| mlr.classif.svm.preproc(4) | 0.871 | 0.871 | 0.871 | 0.871 | 0.871 | 0.871 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco… | 0.869 | 0.869 | 0.869 | 0.869 | 0.869 | 0.869 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2) | 0.8625 | 0.8625 | 0.8625 | 0.8625 | 0.8625 | 0.8625 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.842 | 0.842 | 0.842 | 0.842 | 0.842 | 0.842 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression… | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 0.8415 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 1 |
| weka.SMO_PolyKernel(1) | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 0.8405 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.839 | 0.839 | 0.839 | 0.839 | 0.839 | 0.839 | 1 |
| weka.SMO_RBFKernel(1) | 0.839 | 0.839 | 0.839 | 0.839 | 0.839 | 0.839 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.8385 | 0.8385 | 0.8385 | 0.8385 | 0.8385 | 0.8385 | 1 |
| weka.SMO_PolyKernel(15) | 0.8375 | 0.8375 | 0.8375 | 0.8375 | 0.8375 | 0.8375 | 1 |
| weka.FilteredClassifier_SMO_PolyKernel(1) | 0.8365 | 0.8365 | 0.8365 | 0.8365 | 0.8365 | 0.8365 | 1 |
| weka.FilteredClassifier_AttributeSelectedClassifier_MultilayerPerceptron(1) | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 1 |
| weka.AttributeSelectedClassifier_MultilayerPerceptron(2) | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 1 |
| classif.rda(7) | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 0.836 | 1 |
| classif.svm(7) | 0.8355 | 0.8355 | 0.8355 | 0.8355 | 0.8355 | 0.8355 | 1 |
…and 8 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 (33)
- classif.ksvm(6)
- classif.rda(7)
- classif.svm(7)
- mlr.classif.rda.preproc(2)
- mlr.classif.svm.preproc(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=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,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=openmlcontrib.preprocessing.imputation.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,v…
- 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(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,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)(2)
- 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,logisticregression=sklearn.linear_model.logistic.LogisticRegression…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression…
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- weka.AttributeSelectedClassifier_MultilayerPerceptron(2)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.FilteredClassifier_AttributeSelectedClassifier_MultilayerPerceptron(1)
- weka.FilteredClassifier_MultilayerPerceptron(2)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.FilteredClassifier_SMO_PolyKernel(1)
- weka.MultilayerPerceptron(8)
- weka.SMO_PolyKernel(1)
- weka.SMO_PolyKernel(15)
- weka.SMO_RBFKernel(1)
Published by OpenML.
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