OpenML-CC18: pc3
Best predictive accuracy per machine-learning flow on the pc3 classification task from the OpenML-CC18 suite.
Community results for the pc3 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3903). 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 44 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,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 1 |
| mlr.classif.RRF.imputed.dummied.preproc(1) | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 0.9091 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.9085 | 0.9085 | 0.9085 | 0.9085 | 0.9085 | 0.9085 | 1 |
| mlr.classif.ranger(9) | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 1 |
| mlr.classif.ranger(15) | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 0.9079 | 1 |
| mlr.classif.ranger(16) | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 1 |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi… | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol… | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 0.9072 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| sklearn.svm.classes.SVC(4) | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2) | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| sklearn.svm.classes.SVC(32) | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 0.9066 | 1 |
| weka.classifiers.trees.RandomForest(1) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| weka.kf.RandomForest(1) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(16) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| mlr.classif.randomForest.preproc(5) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| weka.RandomForest(9) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| classif.randomForest(43) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| weka.RandomForest(5) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
| weka.RandomForest(2) | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 0.906 | 1 |
…and 19 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 (44)
- classif.extraTrees(5)
- classif.randomForest(43)
- classif.ranger(5)
- mlr.classif.randomForest.preproc(5)
- mlr.classif.randomForestSRC.preproc(2)
- mlr.classif.ranger(13)
- mlr.classif.ranger(15)
- mlr.classif.ranger(16)
- mlr.classif.ranger(9)
- mlr.classif.RRF.imputed.dummied.preproc(1)
- mlr.classif.RRF.preproc(2)
- mlr.classif.svm(6)
- mlr.classif.svm(7)
- sklearn.ensemble.forest.RandomForestClassifier(13)
- 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=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(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(…
- sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
- sklearn.svm.classes.SVC(32)
- sklearn.svm.classes.SVC(4)
- weka.AdaBoostM1_RandomForest(2)
- weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest(2)
- weka.Bagging_RandomForest(2)
- weka.Bagging_RandomTree(2)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.classifiers.trees.RandomForest(1)
- weka.FilteredClassifier_MultiSearch_RandomForest(1)
- weka.kf.RandomForest(1)
- weka.LibSVM(2)
- weka.LibSVM(7)
- weka.LibSVM(8)
- weka.OLM(3)
- weka.OLM(4)
- weka.RandomForest(2)
- weka.RandomForest(5)
- weka.RandomForest(9)
Published by OpenML.
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