OpenML-CC18: pc1
Best predictive accuracy per machine-learning flow on the pc1 classification task from the OpenML-CC18 suite.
Community results for the pc1 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3918). 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 10 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| mlr.classif.ranger(13) | 0.945 | 0.945 | 0.945 | 0.945 | 0.945 | 0.945 | 1 |
| mlr.classif.ranger(9) | 0.9432 | 0.9432 | 0.9432 | 0.9432 | 0.9432 | 0.9432 | 1 |
| mlr.classif.svm(6) | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 1 |
| mlr.classif.ranger(7) | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 0.9423 | 1 |
| mlr.classif.ranger(16) | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 1 |
| mlr.classif.ranger(15) | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 0.9414 | 1 |
| mlr.classif.xgboost(9) | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 1 |
| mlr.classif.svm(7) | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 0.9405 | 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 (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(9)
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
Published by OpenML.
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