OpenML-CC18: mfeat-factors
Best predictive accuracy per machine-learning flow on the mfeat-factors classification task from the OpenML-CC18 suite.
Community results for the mfeat-factors classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/12). 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 25 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 0.984 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1) | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 1 |
| weka.Logistic(8) | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 0.9825 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression… | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| weka.FilteredClassifier_AttributeSelectedClassifier_SMO_PolyKernel(1) | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| weka.AttributeSelectedClassifier_SMO_PolyKernel(2) | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| mlr.classif.svm.preproc.preproc.tuned(5) | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 0.982 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| weka.FilteredClassifier_MultiSearch_Logistic(1) | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| weka.Bagging_LMT(2) | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| weka.Bagging_LMT(1) | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 0.9815 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression… | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| mlr.classif.lda.preproc(2) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| mlr.classif.lda.preproc(1) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| classif.lda(11) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| classif.sda.tuned(2) | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 1 |
| optimus_ml.optimizer.model_optimizer.ModelOptimizer(estimator=sklearn.svm.classes.SVC)(2) | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1) | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1) | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 0.9805 | 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 (25)
- classif.lda(11)
- classif.sda.tuned(2)
- mlr.classif.lda.preproc(1)
- mlr.classif.lda.preproc(2)
- mlr.classif.svm.preproc.preproc.tuned(5)
- optimus_ml.optimizer.model_optimizer.ModelOptimizer(estimator=sklearn.svm.classes.SVC)(2)
- 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,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…
- sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
- sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…
- 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)
- 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.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)
- weka.AttributeSelectedClassifier_SMO_PolyKernel(2)
- weka.Bagging_LMT(1)
- weka.Bagging_LMT(2)
- weka.FilteredClassifier_AttributeSelectedClassifier_SMO_PolyKernel(1)
- weka.FilteredClassifier_MultiSearch_Logistic(1)
- weka.Logistic(8)
Published by OpenML.
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