OpenML-CC18: Fashion-MNIST

Community results for the Fashion-MNIST classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/146825). 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 60 measurements, ranked by average.

…and 35 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 (60)

• automlbenchmark_autosklearn(1)
• automlbenchmark_autoweka(1)
• automlbenchmark_h2oautoml(1)
• automlbenchmark_randomforest(1)
• automlbenchmark_tpot(1)
• automlbenchmark_tunedrandomforest(1)
• bsplitz.DecisionStumpClassifierV3(1)
• keras.engine.sequential.Sequential.5F750FE4F9E4AAE0(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,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)(3)
• 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,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation)(2)
• 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(32)
• sklearn.ensemble.forest.RandomForestClassifier(35)
• sklearn.ensemble.forest.RandomForestClassifier(57)
• sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(22)
• sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(6)
• sklearn.ensemble._weight_boosting.AdaBoostClassifier(1)
• sklearn.linear_model._logistic.LogisticRegression(1)
• sklearn.linear_model._logistic.LogisticRegression(2)
• sklearn.linear_model.logistic.LogisticRegression(29)
• sklearn.linear_model.logistic.LogisticRegression(30)
• sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
• sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
• sklearn.naive_bayes.GaussianNB(8)
• sklearn.neighbors.classification.KNeighborsClassifier(25)
• sklearn.neighbors.classification.KNeighborsClassifier(34)
• sklearn.neural_network.multilayer_perceptron.MLPClassifier(16)
• sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHot…
• sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._fastfood.Fastfood,svm=sklearn.svm.classes.LinearSVC)(1)
• sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_thresho…
• 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=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(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,model=sklearn.tree._classes.DecisionTreeClassifier)(2)
• sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)
• sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)
• sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)
• sklearn.pipeline.Pipeline(scale=sklearn.preprocessing.data.StandardScaler,SVC=sklearn.svm.classes.LinearSVC)(1)

…and 10 more.

Published by OpenML.