OpenML-CC18: Internet-Advertisements
Best predictive accuracy per machine-learning flow on the Internet-Advertisements classification task from the OpenML-CC18 suite.
Community results for the Internet-Advertisements classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/167125). 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.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 1 |
| weka.classifiers.trees.RandomForest(1) | 0.9799 | 0.9799 | 0.9799 | 0.9799 | 0.9799 | 0.9799 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.979 | 0.979 | 0.979 | 0.979 | 0.979 | 0.979 | 1 |
| sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4) | 0.9765 | 0.9765 | 0.9765 | 0.9765 | 0.9765 | 0.9765 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.9741 | 0.9741 | 0.9741 | 0.9741 | 0.9741 | 0.9741 | 1 |
| weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3) | 0.9738 | 0.9738 | 0.9738 | 0.9738 | 0.9738 | 0.9738 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,model=sklearn.tree._classes.DecisionTreeClassifier)(2) | 0.9695 | 0.9695 | 0.9695 | 0.9695 | 0.9695 | 0.9695 | 1 |
| weka.classifiers.trees.J48(1) | 0.9674 | 0.9674 | 0.9674 | 0.9674 | 0.9674 | 0.9674 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1) | 0.9655 | 0.9655 | 0.9655 | 0.9655 | 0.9655 | 0.9655 | 1 |
| mlr.classif.rpart(47) | 0.9652 | 0.9652 | 0.9652 | 0.9652 | 0.9652 | 0.9652 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.964 | 0.964 | 0.964 | 0.964 | 0.964 | 0.964 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9604 | 0.9604 | 0.9604 | 0.9604 | 0.9604 | 0.9604 | 1 |
| weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic)(1) | 0.9597 | 0.9597 | 0.9597 | 0.9597 | 0.9597 | 0.9597 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9591 | 0.9591 | 0.9591 | 0.9591 | 0.9591 | 0.9591 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.9539 | 0.9539 | 0.9539 | 0.9539 | 0.9539 | 0.9539 | 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.rpart(47)
- sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
- sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,model=sklearn.tree._classes.DecisionTreeClassifier)(2)
- 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,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(1)
- 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)
- weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic)(1)
- weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.classifiers.trees.J48(1)
- weka.classifiers.trees.RandomForest(1)
Published by OpenML.
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