OpenML-CC18: kr-vs-kp
Best predictive accuracy per machine-learning flow on the kr-vs-kp classification task from the OpenML-CC18 suite.
Community results for the kr-vs-kp classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3). 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 19 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| mlr.classif.svm(6) | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 1 |
| mlr.classif.svm(7) | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 0.9981 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1) | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 0.9978 | 1 |
| weka.AttributeSelectedClassifier_SMO_PolyKernel(2) | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 1 |
| weka.SMO_RBFKernel(1) | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(2) | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | 1 |
| mlr.classif.ranger(16) | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| mlr.classif.ranger(9) | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| mlr.classif.ranger(13) | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| weka.MultiBoostAB_JRip(1) | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 0.9972 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 0.9969 | 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 (19)
- mlr.classif.ranger(13)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.svm(6)
- mlr.classif.svm(7)
- sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
- 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(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,variencethreshold=sklearn.feature_selection.variance_threshold…
- sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold…
- sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(2)
- weka.AttributeSelectedClassifier_SMO_PolyKernel(2)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.MultiBoostAB_JRip(1)
- weka.SMO_RBFKernel(1)
Published by OpenML.
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