smplmark

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 target 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.

Metrics

  • predictive_accuracy

Targets (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.