OpenML-CC18: breast-w
Best predictive accuracy per machine-learning flow on the breast-w classification task from the OpenML-CC18 suite.
Community results for the breast-w classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/15). 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 54 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| weka.AttributeSelectedClassifier_Bagging_BayesNet(1) | 0.9771 | 0.9771 | 0.9771 | 0.9771 | 0.9771 | 0.9771 | 1 |
| weka-rotationforest-principalcomponents-j48-14 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| weka-rotationforest-principalcomponents-j48-3 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 0.9757 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2) | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1) | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.Varia… | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 0.9742 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| weka-kf-replacemissingvalues-pkidiscretize-naivebayes-1 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| mlr.classif.ranger.imputed.dummied.preproc(1) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| weka.AttributeSelectedClassifier_NaiveBayes(2) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| mlr.classif.randomForestSRC(2) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
| weka.FilteredClassifier_MultiSearch_RandomForest(1) | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 1 |
…and 29 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 (54)
- classif.randomForestSRC(10)
- classif.ranger(8)
- mlr.classif.earth.imputed.preproc(1)
- mlr.classif.randomForestSRC(2)
- mlr.classif.ranger.imputed.dummied.preproc(1)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- 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(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(dualimputer=extra.dual_imputer.DualImputer,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,neuralnetwork=helper.nn.NeuralNetwork)(1)
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol…
- 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,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=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.Varia…
- sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2)
- sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- weka.A1DE(2)
- weka.A1DE(28)
- weka.A1DE(4)
- weka.AttributeSelectedClassifier_A1DE(1)
- weka.AttributeSelectedClassifier_Bagging_BayesNet(1)
- weka.AttributeSelectedClassifier_NaiveBayes(2)
- weka.AttributeSelectedClassifier_PrincipalComponents_Ranker_J48(1)
- weka.Bagging_RandomForest(2)
- weka.Bagging_RandomForest(9)
- weka.FilteredClassifier_A1DE(1)
- weka.FilteredClassifier_AttributeSelectedClassifier_A1DE(1)
- weka.FilteredClassifier_AttributeSelectedClassifier_IBk(1)
- weka.FilteredClassifier_IBk(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.FilteredClassifier_PrincipalComponents_J48(1)
- weka.IBk(1)
- weka.IBk(4)
- weka.kf.AttributeSelection-BestFirst-CfsSubsetEval-Standardize-IBk5(1)
- weka.kf.AttributeSelection-Ranker-InfoGain-Standardize-IBk5(1)
- weka.kf.AttributeSelection-Ranker-ReliefF-Standardize-IBk5(1)
…and 4 more.
Published by OpenML.
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