OpenML-CC18: qsar-biodeg
Best predictive accuracy per machine-learning flow on the qsar-biodeg classification task from the OpenML-CC18 suite.
Community results for the qsar-biodeg classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9957). 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 13 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.8938 | 0.8938 | 0.8938 | 0.8938 | 0.8938 | 0.8938 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.8929 | 0.8929 | 0.8929 | 0.8929 | 0.8929 | 0.8929 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8919 | 0.8919 | 0.8919 | 0.8919 | 0.8919 | 0.8919 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.89 | 0.89 | 0.89 | 0.89 | 0.89 | 0.89 | 1 |
| mlr.classif.svm(6) | 0.8891 | 0.8891 | 0.8891 | 0.8891 | 0.8891 | 0.8891 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1) | 0.8882 | 0.8882 | 0.8882 | 0.8882 | 0.8882 | 0.8882 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 0.8863 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.8853 | 0.8853 | 0.8853 | 0.8853 | 0.8853 | 0.8853 | 1 |
| mlr.classif.xgboost(9) | 0.8844 | 0.8844 | 0.8844 | 0.8844 | 0.8844 | 0.8844 | 1 |
| weka.KernelLogisticRegression_RBFKernel(3) | 0.8834 | 0.8834 | 0.8834 | 0.8834 | 0.8834 | 0.8834 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 0.8825 | 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 (13)
- mlr.classif.svm(6)
- mlr.classif.xgboost(9)
- 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=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,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)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)
- weka.KernelLogisticRegression_RBFKernel(3)
Published by OpenML.
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