OpenML-CC18: CIFAR_10
Best predictive accuracy per machine-learning flow on the CIFAR_10 classification task from the OpenML-CC18 suite.
Community results for the CIFAR_10 classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/167124). 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 28 measurements, ranked by average.
| Subject | Avg | Median | Min | Max | P95 | P99 | Count |
|---|---|---|---|---|---|---|---|
| keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dr… | 0.7255 | 0.7255 | 0.7255 | 0.7255 | 0.7255 | 0.7255 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Fl… | 0.7244 | 0.7244 | 0.7244 | 0.7244 | 0.7244 | 0.7244 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Fl… | 0.661 | 0.661 | 0.661 | 0.661 | 0.661 | 0.661 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1) | 0.5874 | 0.5874 | 0.5874 | 0.5874 | 0.5874 | 0.5874 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dr… | 0.5314 | 0.5314 | 0.5314 | 0.5314 | 0.5314 | 0.5314 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(2) | 0.5223 | 0.5223 | 0.5223 | 0.5223 | 0.5223 | 0.5223 | 1 |
| keras.engine.sequential.Sequential.C472F04AB8E1D532(1) | 0.499 | 0.499 | 0.499 | 0.499 | 0.499 | 0.499 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalizat… | 0.4842 | 0.4842 | 0.4842 | 0.4842 | 0.4842 | 0.4842 | 1 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho… | 0.4624 | 0.4624 | 0.4624 | 0.4624 | 0.4624 | 0.4624 | 1 |
| keras.engine.sequential.Sequential.3aeb2942(1) | 0.4398 | 0.4398 | 0.4398 | 0.4398 | 0.4398 | 0.4398 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation)(5) | 0.4003 | 0.4003 | 0.4003 | 0.4003 | 0.4003 | 0.4003 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,… | 0.3852 | 0.3852 | 0.3852 | 0.3852 | 0.3852 | 0.3852 | 1 |
| sklearn.discriminant_analysis.LinearDiscriminantAnalysis(2) | 0.376 | 0.376 | 0.376 | 0.376 | 0.376 | 0.376 | 1 |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) | 0.3645 | 0.3645 | 0.3645 | 0.3645 | 0.3645 | 0.3645 | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(35) | 0.3578 | 0.3578 | 0.3578 | 0.3578 | 0.3578 | 0.3578 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,… | 0.2937 | 0.2937 | 0.2937 | 0.2937 | 0.2937 | 0.2937 | 1 |
| sklearn.naive_bayes.GaussianNB(8) | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 1 |
| sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.naive_bayes.GaussianNB)(1) | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 0.2865 | 1 |
| sklearn.tree.tree.DecisionTreeClassifier(22) | 0.2673 | 0.2673 | 0.2673 | 0.2673 | 0.2673 | 0.2673 | 1 |
| mlr.classif.rpart(47) | 0.2249 | 0.2249 | 0.2249 | 0.2249 | 0.2249 | 0.2249 | 1 |
| keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concate… | 0.1671 | 0.1671 | 0.1671 | 0.1671 | 0.1671 | 0.1671 | 1 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot… | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 1 |
| sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1) | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 0.1557 | 1 |
| tensorflow.python.keras.engine.sequential.Sequential.5d790f77(1) | 0.1328 | 0.1328 | 0.1328 | 0.1328 | 0.1328 | 0.1328 | 1 |
| tensorflow.python.keras.engine.sequential.Sequential.12702f59(1) | 0.1223 | 0.1223 | 0.1223 | 0.1223 | 0.1223 | 0.1223 | 1 |
…and 3 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 (28)
- keras.engine.sequential.Sequential.3aeb2942(1)
- keras.engine.sequential.Sequential.54cdbc1b(1)
- keras.engine.sequential.Sequential.54cdbc1b(2)
- keras.engine.sequential.Sequential.6fe33635(1)
- keras.engine.sequential.Sequential.C472F04AB8E1D532(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,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalizat…
- keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,…
- keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,…
- 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)
- keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dr…
- keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dr…
- mlr.classif.rpart(47)
- sklearn.discriminant_analysis.LinearDiscriminantAnalysis(2)
- sklearn.ensemble.forest.RandomForestClassifier(35)
- sklearn.naive_bayes.GaussianNB(8)
- sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot…
- sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho…
- sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.naive_bayes.GaussianNB)(1)
- sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)
- sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)
- sklearn.tree.tree.DecisionTreeClassifier(22)
- tensorflow.python.keras.engine.sequential.Sequential.12702f59(1)
- tensorflow.python.keras.engine.sequential.Sequential.5d790f77(1)
Published by OpenML.
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