OpenML-CC18: jungle_chess_2pcs_raw_endgame_complete

Best predictive accuracy per machine-learning flow on the jungle_chess_2pcs_raw_endgame_complete classification task from the OpenML-CC18 suite.

Community results for the jungle_chess_2pcs_raw_endgame_complete classification task from OpenML-CC18, OpenML's curated suite of 72 classification tasks (www.openml.org/t/167119). 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 29 measurements, ranked by average.

SubjectAvgMedianMinMaxP95P99Count
automlbenchmark_tpot(1)0.93950.93950.93950.93950.93950.93951
automlbenchmark_h2oautoml(1)0.93530.93530.93530.93530.93530.93531
automlbenchmark_autosklearn(1)0.89730.89730.89730.89730.89730.89731
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)0.88780.88780.88780.88780.88780.88781
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)0.8780.8780.8780.8780.8780.8781
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)0.87410.87410.87410.87410.87410.87411
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)0.87410.87410.87410.87410.87410.87411
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)0.86850.86850.86850.86850.86850.86851
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3)0.86840.86840.86840.86840.86840.86841
sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)0.86830.86830.86830.86830.86830.86831
sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)0.86730.86730.86730.86730.86730.86731
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,model=sklearn.tree._classes.DecisionTreeClassifier)(2)0.8640.8640.8640.8640.8640.8641
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)0.86220.86220.86220.86220.86220.86221
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,varienceth…0.86190.86190.86190.86190.86190.86191
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethr…0.86170.86170.86170.86170.86170.86171
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…0.85990.85990.85990.85990.85990.85991
weka.classifiers.trees.J48(1)0.85910.85910.85910.85910.85910.85911
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethre…0.85880.85880.85880.85880.85880.85881
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…0.85590.85590.85590.85590.85590.85591
sklearn.svm.classes.SVC(45)0.85340.85340.85340.85340.85340.85341
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1)0.8410.8410.8410.8410.8410.8411
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(4)0.84060.84060.84060.84060.84060.84061
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro)(1)0.83780.83780.83780.83780.83780.83781
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…0.83650.83650.83650.83650.83650.83651
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)0.83550.83550.83550.83550.83550.83551

…and 4 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 (29)

  • arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)
  • automlbenchmark_autosklearn(1)
  • automlbenchmark_h2oautoml(1)
  • automlbenchmark_tpot(1)
  • sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)
  • sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
  • sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
  • sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(4)
  • sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(3)
  • sklearn.neural_network.multilayer_perceptron.MLPClassifier(18)
  • sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=skl…
  • 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,variencethreshold=sklearn.feature_selection.variance_thresho…
  • sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshol…
  • 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(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,model=sklearn.tree._classes.DecisionTreeClassifier)(2)
  • sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)
  • sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(4)
  • 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.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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifi…
  • sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)
  • sklearn.svm.classes.SVC(45)
  • weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(3)
  • weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingVa…
  • weka.classifiers.trees.J48(1)

Published by OpenML.