OpenML-CC18: blood-transfusion-service-center
Best predictive accuracy per machine-learning flow on the blood-transfusion-service-center classification task from the OpenML-CC18 suite.
Community results for the blood-transfusion-service-center classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/10101). 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(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neural_network._multilayer_pe… | 0.8075 | 0.8075 | 0.8075 | 0.8075 | 0.8075 | 0.8075 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8048 | 0.8048 | 0.8048 | 0.8048 | 0.8048 | 0.8048 | 1 |
| mlr.classif.ranger(13) | 0.8021 | 0.8021 | 0.8021 | 0.8021 | 0.8021 | 0.8021 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 1 |
| mlr.classif.ranger(9) | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1) | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 0.8008 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.7995 | 0.7995 | 0.7995 | 0.7995 | 0.7995 | 0.7995 | 1 |
| sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._weight_boosting.Ada… | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| sklearn.ensemble._weight_boosting.AdaBoostClassifier(1) | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| mlr.classif.rpart(29) | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| mlr.classif.xgboost(9) | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| classif.bartMachine(3) | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 0.7981 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 1 |
| weka.LMT(4) | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 0.7968 | 1 |
| mlr.classif.ranger(16) | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 1 |
| mlr.classif.rpart.imputed.dummied.preproc(1) | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 1 |
| mlr.classif.rpart.preproc(16) | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 0.7955 | 1 |
| mlr.classif.ranger(15) | 0.7941 | 0.7941 | 0.7941 | 0.7941 | 0.7941 | 0.7941 | 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.bartMachine(3)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.rpart(29)
- mlr.classif.rpart.imputed.dummied.preproc(1)
- mlr.classif.rpart.preproc(16)
- mlr.classif.xgboost(9)
- sklearn.ensemble._weight_boosting.AdaBoostClassifier(1)
- 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(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot…
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot…
- 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(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(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neural_network._multilayer_pe…
- sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._weight_boosting.Ada…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)
- weka.LMT(4)
Published by OpenML.
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