smplmark

OpenML-CC18: mnist_784

Best predictive accuracy per machine-learning flow on the mnist_784 classification task from the OpenML-CC18 suite.

Community results for the mnist_784 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/3573). 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 (26)

  • keras.engine.sequential.Sequential.36D3EA08BAB37560(1)
  • keras.engine.sequential.Sequential.C95B7C6AC5809092(1)
  • keras.wrappers.scikit_learn.KerasClassifier(2)
  • keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concate…
  • keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Ma…
  • keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Ma…
  • keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Fl…
  • keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Fl…
  • keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation)(5)
  • mxnet.gluon.nn.basic_layers.HybridSequential.75709f3c(1)
  • scikeras.wrappers.KerasClassifier(1)
  • sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
  • sklearn.pipeline.Pipeline(clf=keras.wrappers.scikit_learn.KerasClassifier)(1)
  • 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(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,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
  • sklearn.svm.classes.SVC(32)
  • torch.nn.modules.container.Sequential.4302e8192bf61705(1)
  • torch.nn.modules.container.Sequential.9bf9509fc1a9ea04(1)
  • torch.nn.modules.container.Sequential.e5f895589155e3c(1)
  • torch.nn.Sequential.bb8de941c9933dd2(1)
  • weka.SMO_RBFKernel(2)
  • weka.SMO_RBFKernel(3)

Published by OpenML.