OpenML-CC18: analcatdata_authorship
Best predictive accuracy per machine-learning flow on the analcatdata_authorship classification task from the OpenML-CC18 suite.
Community results for the analcatdata_authorship classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3549). 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 19 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.FilteredClassifier_MultiSearch_MultilayerPerceptron(1) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.SMO_NormalizedPolyKernel(1) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.A2DE(5) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.MultilayerPerceptron(4) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.A1DE(28) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| classif.sda(7) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.A1DE(4) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.A2DE(3) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.MultiBoostAB_MultilayerPerceptron(2) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.A2DE(2) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| weka.Bagging_MultilayerPerceptron(2) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression… | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression… | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.svm.classes.SVC(32) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold… | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 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 (19)
- classif.sda(7)
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
- 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(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,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression…
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression…
- sklearn.svm.classes.SVC(32)
- weka.A1DE(28)
- weka.A1DE(4)
- weka.A2DE(2)
- weka.A2DE(3)
- weka.A2DE(5)
- weka.Bagging_MultilayerPerceptron(2)
- weka.FilteredClassifier_MultiSearch_MultilayerPerceptron(1)
- weka.MultiBoostAB_MultilayerPerceptron(2)
- weka.MultilayerPerceptron(4)
- weka.SMO_NormalizedPolyKernel(1)
Published by OpenML.
No observations in this window.