OpenML-CC18: vehicle

Best predictive accuracy per machine-learning flow on the vehicle classification task from the OpenML-CC18 suite.

Community results for the vehicle classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/53). 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 32 measurements, ranked by average.

SubjectAvgMedianMinMaxP95P99Count
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…0.870.870.870.870.870.871
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…0.86880.86880.86880.86880.86880.86881
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.86640.86640.86640.86640.86640.86641
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,…0.86520.86520.86520.86520.86520.86521
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…0.86410.86410.86410.86410.86410.86411
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.86290.86290.86290.86290.86290.86291
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…0.85930.85930.85930.85930.85930.85931
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…0.85820.85820.85820.85820.85820.85821
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…0.85820.85820.85820.85820.85820.85821
weka.LWL_Logistic(1)0.85580.85580.85580.85580.85580.85581
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.85580.85580.85580.85580.85580.85581
weka.AdaBoostM1_LMT(2)0.85580.85580.85580.85580.85580.85581
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)0.85340.85340.85340.85340.85340.85341
weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)0.85340.85340.85340.85340.85340.85341
weka.SMO_PolyKernel(1)0.85340.85340.85340.85340.85340.85341
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEnco…0.85340.85340.85340.85340.85340.85341
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.85110.85110.85110.85110.85110.85111
weka.AdaBoostM1_LMT(1)0.84870.84870.84870.84870.84870.84871
mlr.classif.svm.preproc.preproc.tuned(25)0.84750.84750.84750.84750.84750.84751
weka.Bagging_MultilayerPerceptron(3)0.84750.84750.84750.84750.84750.84751
weka.MultilayerPerceptron(1)0.84750.84750.84750.84750.84750.84751
mlr.classif.qda.preproc(3)0.84630.84630.84630.84630.84630.84631
mlr.classif.qda.preproc(2)0.84630.84630.84630.84630.84630.84631
weka.FilteredClassifier_MultiSearch_MultilayerPerceptron(1)0.84630.84630.84630.84630.84630.84631
classif.qda(2)0.84630.84630.84630.84630.84630.84631

…and 7 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 (32)

  • classif.qda(2)
  • mlr.classif.qda.preproc(2)
  • mlr.classif.qda.preproc(3)
  • mlr.classif.svm.preproc.preproc.tuned(25)
  • 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(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,variencethreshold=sklearn.feature_selection.variance_threshold…
  • 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)(1)
  • 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)
  • weka.AdaBoostM1_LMT(1)
  • weka.AdaBoostM1_LMT(2)
  • weka.Bagging_MultilayerPerceptron(2)
  • weka.Bagging_MultilayerPerceptron(3)
  • weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
  • weka.FilteredClassifier_MultiSearch_MultilayerPerceptron(1)
  • weka.FilteredClassifier_MultiSearch_SMO_RBFKernel(1)
  • weka.FilteredClassifier_SMO_PolyKernel(1)
  • weka.LWL_Logistic(1)
  • weka.MultiBoostAB_LMT(1)
  • weka.MultilayerPerceptron(1)
  • weka.SMO_PolyKernel(1)
  • weka.SMO_PolyKernel(11)
  • weka.SMO_PolyKernel(15)
  • weka.SMO_RBFKernel(1)

Published by OpenML.