OpenML-CC18: kc2
Best predictive accuracy per machine-learning flow on the kc2 classification task from the OpenML-CC18 suite.
Community results for the kc2 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3913). 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 33 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.8697 | 0.8697 | 0.8697 | 0.8697 | 0.8697 | 0.8697 | 1 |
| mlr.classif.xgboost(9) | 0.8621 | 0.8621 | 0.8621 | 0.8621 | 0.8621 | 0.8621 | 1 |
| mlr.classif.ranger(13) | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 1 |
| mlr.classif.xgboost(6) | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 1 |
| sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1) | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 0.8582 | 1 |
| weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(2) | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1) | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 0.8563 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 1 |
| weka.LogitBoost_DecisionStump(3) | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 1 |
| weka.LogitBoost_DecisionStump(2) | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 0.8544 | 1 |
| sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardSca… | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v… | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| mlr.classif.ranger(9) | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
| mlr.classif.ranger(16) | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 0.8525 | 1 |
…and 8 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 (33)
- mlr.classif.glmnet(11)
- mlr.classif.glmnet(5)
- mlr.classif.kknn(10)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(7)
- mlr.classif.ranger(9)
- mlr.classif.svm(6)
- mlr.classif.svm(7)
- mlr.classif.xgboost(6)
- mlr.classif.xgboost(9)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,v…
- 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(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(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(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(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardSca…
- 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…
- weka.Bagging_REPTree(8)
- weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)
- weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(2)
- weka.LogitBoost_DecisionStump(2)
- weka.LogitBoost_DecisionStump(3)
Published by OpenML.
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