OpenML

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

424 runs0 likes1 downloads1 reach0 impact

weka.CostSensitiveClassifier_Bagging_RandomTree (1)

Weka implementation of CostSensitiveClassifier

423 runs0 likes1 downloads1 reach426 impact

weka.FilteredClassifier_J48 (3)

Weka implementation of FilteredClassifier

417 runs0 likes0 downloads0 reach416 impact

moa.SGD (1)

Moa implementation of SGD

417 runs0 likes2 downloads2 reach41 impact

weka.DecisionStump (5)

Weka implementation of DecisionStump

412 runs0 likes3 downloads3 reach214 impact

weka.OneR (18)

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.

404 runs0 likes2 downloads2 reach8 impact

weka.SMO_RBFKernel (4)

J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…

400 runs0 likes3 downloads3 reach361 impact

weka.OneR (1)

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.

397 runs0 likes3 downloads3 reach360 impact

weka.LogitBoost_DecisionStump (6)

J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

396 runs0 likes0 downloads0 reach396 impact

weka.RandomForest (12)

Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

391 runs1 likes0 downloads1 reach390 impact

weka.RandomForest (1)

Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

389 runs1 likes20 downloads21 reach360 impact

weka.AttributeSelectedClassifier_J48 (2)

Weka implementation of AttributeSelectedClassifier

387 runs0 likes0 downloads0 reach386 impact

weka.AttributeSelectedClassifier_IBk (1)

Weka implementation of AttributeSelectedClassifier

385 runs0 likes1 downloads1 reach0 impact

weka.ThresholdSelector_Logistic (1)

Weka implementation of ThresholdSelector

381 runs0 likes1 downloads1 reach0 impact

weka.RealAdaBoost_NaiveBayes (1)

J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

374 runs0 likes2 downloads2 reach102 impact

sklearn.model_selection._search_successive_halving.HalvingRandomSearchCV(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (3)

Randomized search on hyper parameters. The search strategy starts evaluating all the candidates with a small amount of resources and iteratively selects the best candidates, using more and more…

369 runs0 likes1 downloads1 reach0 impact

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro) (1)

Automatically created scikit-learn flow.

365 runs0 likes0 downloads0 reach0 impact

weka.Bagging_RandomForest (9)

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

363 runs0 likes2 downloads2 reach363 impact

weka.Stacking_RandomTree (2)

David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.

353 runs0 likes2 downloads2 reach222 impact

weka.NaiveBayesUpdateable (10)

George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.

341 runs0 likes2 downloads2 reach341 impact

weka.AdaBoostM1_DecisionStump (1)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

341 runs0 likes3 downloads3 reach316 impact

weka.RealAdaBoost_RandomTree (1)

J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

339 runs0 likes2 downloads2 reach54 impact

weka.LogitBoost_DecisionStump (1)

J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

339 runs0 likes3 downloads3 reach309 impact

moa.Perceptron (1)

Moa implementation of Perceptron

337 runs0 likes3 downloads3 reach49 impact

weka.AttributeSelectedClassifier_RandomForest (2)

Weka implementation of AttributeSelectedClassifier

336 runs0 likes0 downloads0 reach334 impact

weka.FilteredClassifier_AttributeSelectedClassifier_J48 (1)

Weka implementation of FilteredClassifier

336 runs0 likes0 downloads0 reach335 impact

weka.SMO_PolyKernel (15)

J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…

335 runs0 likes1 downloads1 reach330 impact

weka.LMT (4)

Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European…

335 runs0 likes3 downloads3 reach79 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._logistic.LogisticRegression) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

330 runs0 likes0 downloads0 reach0 impact

mlr.classif.featureless (3)

Learner mlr.classif.featureless from package(s) mlr.

320 runs0 likes0 downloads0 reach0 impact

mlr.classif.ranger (11)

Learner mlr.classif.ranger from package(s) ranger.

319 runs0 likes0 downloads0 reach0 impact

weka.Bagging_REPTree (17)

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

314 runs0 likes2 downloads2 reach314 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

312 runs0 likes0 downloads0 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neighbors._classification.KNeighborsClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

311 runs0 likes0 downloads0 reach0 impact

weka.AttributeSelectedClassifier_NaiveBayes (2)

Weka implementation of AttributeSelectedClassifier

310 runs0 likes0 downloads0 reach309 impact

weka.LADTree (7)

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

308 runs0 likes2 downloads2 reach17 impact

weka.IBk (11)

D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.

306 runs0 likes0 downloads0 reach304 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

306 runs0 likes0 downloads0 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.naive_bayes.GaussianNB) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

306 runs0 likes0 downloads0 reach0 impact

classif.J48 (20)

Learner classif.J48 from package(s) RWeka.

304 runs0 likes1 downloads1 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.svm._classes.SVC) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

304 runs0 likes0 downloads0 reach0 impact

weka.RealAdaBoost_Logistic (1)

J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

303 runs0 likes2 downloads2 reach55 impact

weka.PART (4)

Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.

302 runs0 likes3 downloads3 reach302 impact

moa.WEKAClassifier_J48 (1)

Moa implementation of WEKAClassifier

301 runs0 likes2 downloads2 reach42 impact

classif.IBk (5)

Learner classif.IBk from package(s) RWeka.

300 runs0 likes1 downloads1 reach0 impact

classif.JRip (7)

Learner classif.JRip from package(s) RWeka.

300 runs0 likes1 downloads1 reach0 impact

classif.kknn (2)

Learner classif.kknn from package(s) !kknn.

300 runs0 likes1 downloads1 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._forest.RandomForestClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

300 runs0 likes0 downloads0 reach0 impact

mlr.classif.ksvm (6)

Learner mlr.classif.ksvm from package(s) kernlab.

299 runs0 likes0 downloads0 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

299 runs0 likes0 downloads0 reach0 impact

weka.AdaBoostM1_J48 (3)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

298 runs0 likes2 downloads2 reach31 impact

classif.avNNet (5)

Learner classif.avNNet from package(s) nnet.

298 runs0 likes1 downloads1 reach0 impact

weka.FilteredClassifier_AttributeSelectedClassifier_RandomForest (1)

Weka implementation of FilteredClassifier

298 runs0 likes0 downloads0 reach297 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._gb.GradientBoostingClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

298 runs0 likes0 downloads0 reach0 impact

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._forest.ExtraTreesClassifier) (1)

Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…

298 runs0 likes0 downloads0 reach0 impact

classif.gbm (3)

Learner classif.gbm from package(s) gbm.

297 runs0 likes2 downloads2 reach1 impact

classif.svm (5)

Learner classif.svm from package(s) e1071.

297 runs0 likes2 downloads2 reach0 impact

sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC)) (1)

Automatically created scikit-learn flow.

297 runs0 likes0 downloads0 reach0 impact

classif.ctree (4)

Learner classif.ctree from package(s) party.

296 runs0 likes1 downloads1 reach0 impact

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