OpenML-CC18: madelon
Best predictive accuracy per machine-learning flow on the madelon classification task from the OpenML-CC18 suite.
Community results for the madelon classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/9976). 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 15 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| weka.kf.AttributeSelection-Ranker-ReliefF-Standardize-IBk5(1) | 0.8881 | 0.8881 | 0.8881 | 0.8881 | 0.8881 | 0.8881 | 1 |
| weka.kf.AttributeSelection-Ranker-ReliefF-KStar(1) | 0.8758 | 0.8758 | 0.8758 | 0.8758 | 0.8758 | 0.8758 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(16) | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 0.8746 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.8742 | 0.8742 | 0.8742 | 0.8742 | 0.8742 | 0.8742 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8735 | 0.8735 | 0.8735 | 0.8735 | 0.8735 | 0.8735 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 1 |
| mlr.classif.ranger(13) | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 0.8727 | 1 |
| mlr.classif.ranger(9) | 0.8715 | 0.8715 | 0.8715 | 0.8715 | 0.8715 | 0.8715 | 1 |
| mlr.classif.ranger(15) | 0.8708 | 0.8708 | 0.8708 | 0.8708 | 0.8708 | 0.8708 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8692 | 0.8692 | 0.8692 | 0.8692 | 0.8692 | 0.8692 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8688 | 0.8688 | 0.8688 | 0.8688 | 0.8688 | 0.8688 | 1 |
| mlr.classif.ranger(16) | 0.8681 | 0.8681 | 0.8681 | 0.8681 | 0.8681 | 0.8681 | 1 |
| sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf… | 0.8627 | 0.8627 | 0.8627 | 0.8627 | 0.8627 | 0.8627 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8596 | 0.8596 | 0.8596 | 0.8596 | 0.8596 | 0.8596 | 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 (15)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- sklearn.ensemble.forest.RandomForestClassifier(16)
- 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(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=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- weka.kf.AttributeSelection-Ranker-ReliefF-KStar(1)
- weka.kf.AttributeSelection-Ranker-ReliefF-Standardize-IBk5(1)
Published by OpenML.
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