OpenML-CC18: kc1
Best predictive accuracy per machine-learning flow on the kc1 classification task from the OpenML-CC18 suite.
Community results for the kc1 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3917). 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 18 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.8734 | 0.8734 | 0.8734 | 0.8734 | 0.8734 | 0.8734 | 1 |
| sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.8725 | 0.8725 | 0.8725 | 0.8725 | 0.8725 | 0.8725 | 1 |
| mlr.classif.ranger(9) | 0.871 | 0.871 | 0.871 | 0.871 | 0.871 | 0.871 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 0.8706 | 1 |
| mlr.classif.ranger(13) | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 1 |
| mlr.classif.ranger(16) | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 1 |
| mlr.classif.ranger(15) | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 1 |
| weka.Bagging_J48(2) | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 0.8691 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 0.8687 | 1 |
| mlr.classif.xgboost(9) | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 1 |
| weka.Bagging_RandomForest(2) | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 0.8677 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 0.8672 | 1 |
| mlr.classif.ranger.imputed.dummied.preproc(1) | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 0.8663 | 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 (18)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.ranger.imputed.dummied.preproc(1)
- mlr.classif.xgboost(9)
- 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,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(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- 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,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- weka.Bagging_J48(2)
- weka.Bagging_RandomForest(2)
Published by OpenML.
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