OpenML-CC18: mfeat-morphological
Best predictive accuracy per machine-learning flow on the mfeat-morphological classification task from the OpenML-CC18 suite.
Community results for the mfeat-morphological classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/18). 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 35 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.772 | 0.772 | 0.772 | 0.772 | 0.772 | 0.772 | 1 |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre… | 0.7705 | 0.7705 | 0.7705 | 0.7705 | 0.7705 | 0.7705 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.7695 | 0.7695 | 0.7695 | 0.7695 | 0.7695 | 0.7695 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4) | 0.7625 | 0.7625 | 0.7625 | 0.7625 | 0.7625 | 0.7625 | 1 |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.759 | 0.759 | 0.759 | 0.759 | 0.759 | 0.759 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco… | 0.7575 | 0.7575 | 0.7575 | 0.7575 | 0.7575 | 0.7575 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl… | 0.7565 | 0.7565 | 0.7565 | 0.7565 | 0.7565 | 0.7565 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth… | 0.7545 | 0.7545 | 0.7545 | 0.7545 | 0.7545 | 0.7545 | 1 |
| weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa… | 0.7535 | 0.7535 | 0.7535 | 0.7535 | 0.7535 | 0.7535 | 1 |
| weka.Bagging_MultilayerPerceptron(3) | 0.753 | 0.753 | 0.753 | 0.753 | 0.753 | 0.753 | 1 |
| weka.AttributeSelectedClassifier_MultilayerPerceptron(2) | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 0.7525 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr… | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 1 |
| weka.MultilayerPerceptron(8) | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 1 |
| weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1) | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 0.752 | 1 |
| sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1) | 0.7515 | 0.7515 | 0.7515 | 0.7515 | 0.7515 | 0.7515 | 1 |
| weka.AttributeSelectedClassifier_RandomCommittee_MultilayerPerceptron(1) | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 1 |
| weka.Bagging_MultilayerPerceptron(2) | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 1 |
| weka.SMO_RBFKernel(1) | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 0.751 | 1 |
| weka.RotationForest_PrincipalComponents_J48(14) | 0.7505 | 0.7505 | 0.7505 | 0.7505 | 0.7505 | 0.7505 | 1 |
| sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,… | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 1 |
| weka.FilteredClassifier_AttributeSelectedClassifier_ClassificationViaRegression_M5P(1) | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 1 |
| weka.FilteredClassifier_ClassificationViaRegression_M5P(1) | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 1 |
| weka.ClassificationViaRegression_M5P(3) | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 1 |
| weka.AttributeSelectedClassifier_ClassificationViaRegression_M5P(1) | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 0.7495 | 1 |
…and 10 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 (35)
- classif.lda(9)
- mlr.classif.cvglmnet.preproc(2)
- mlr.classif.lda.preproc(1)
- mlr.classif.lda.preproc(2)
- mlr.classif.svm.preproc.preproc.tuned(9)
- sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…
- 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(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
- sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- 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,svc=sklearn.svm.classes.SVC)(4)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
- weka.AttributeSelectedClassifier_ClassificationViaRegression_M5P(1)
- weka.AttributeSelectedClassifier_MultilayerPerceptron(2)
- weka.AttributeSelectedClassifier_RandomCommittee_MultilayerPerceptron(1)
- weka.Bagging_MultilayerPerceptron(2)
- weka.Bagging_MultilayerPerceptron(3)
- weka.ClassificationViaRegression_M5P(1)
- weka.ClassificationViaRegression_M5P(3)
- weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
- weka.FilteredClassifier_AttributeSelectedClassifier_ClassificationViaRegression_M5P(1)
- weka.FilteredClassifier_ClassificationViaRegression_M5P(1)
- weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
- weka.MultilayerPerceptron(8)
- weka.RotationForest_PrincipalComponents_J48(14)
- weka.RotationForest_PrincipalComponents_J48(3)
- weka.SMO_PolyKernel(1)
- weka.SMO_RBFKernel(1)
Published by OpenML.
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