OpenML
Moa implementation of ASHoeffdingTree
65 runs0 likes2 downloads2 reach33 impact
Learner classif.boosting from package(s) adabag, rpart.
65 runs0 likes1 downloads1 reach0 impact
Moa implementation of DynamicWeightedMajority
64 runs0 likes0 downloads0 reach0 impact
Moa implementation of TargetMean
64 runs0 likes2 downloads2 reach62 impact
Moa implementation of MajorityVoteEnsemble
64 runs0 likes2 downloads2 reach32 impact
Learner classif.avNNet from package(s) nnet.
64 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.cvglmnet.preproc from package(s) glmnet.
63 runs0 likes0 downloads0 reach0 impact
Weka implementation of RandomizableFilteredClassifier
63 runs0 likes0 downloads0 reach61 impact
Learner classif.IBk from package(s) RWeka.
63 runs0 likes1 downloads1 reach0 impact
Moa implementation of HoeffdingTree
63 runs0 likes2 downloads2 reach12 impact
Moa implementation of MajorityClass
63 runs0 likes2 downloads2 reach58 impact
Moa implementation of NoChange
63 runs0 likes2 downloads2 reach47 impact
Moa implementation of RandomHoeffdingTree
63 runs0 likes2 downloads2 reach40 impact
Moa implementation of NaiveBayes
63 runs0 likes0 downloads0 reach2 impact
Automatically created scikit-learn flow.
63 runs0 likes0 downloads0 reach58 impact
Moa implementation of RuleClassifier
62 runs0 likes0 downloads0 reach0 impact
Moa implementation of NoChange
62 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.avNNet.preproc from package(s) nnet.
62 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.nnet.preproc from package(s) nnet.
62 runs0 likes0 downloads0 reach0 impact
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
62 runs0 likes6 downloads6 reach60 impact
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
62 runs0 likes2 downloads2 reach51 impact
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
62 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.lda.preproc from package(s) MASS.
61 runs0 likes0 downloads0 reach0 impact
Moa implementation of AdaHoeffdingOptionTree
61 runs0 likes2 downloads2 reach60 impact
Moa implementation of HoeffdingOptionTree
61 runs0 likes2 downloads2 reach34 impact
Learner classif.J48 from package(s) RWeka.
61 runs0 likes1 downloads1 reach0 impact
Moa implementation of ASHoeffdingTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of HoeffdingAdaptiveTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of RandomHoeffdingTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of MajorityClass
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of Perceptron
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of HoeffdingOptionTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of kNNwithPAW
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of HoeffdingTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of DecisionStump
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of AccuracyWeightedEnsemble
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.regr.develpartykit.cforest from package(s) partykit.
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.develpartykit.cforest from package(s) partykit.
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.regr.ctree from package(s) party.
60 runs0 likes0 downloads0 reach0 impact
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
60 runs0 likes2 downloads2 reach59 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
60 runs0 likes2 downloads2 reach49 impact
S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
60 runs0 likes1 downloads1 reach60 impact
Learner mlr.classif.gausspr.preproc from package(s) kernlab.
59 runs0 likes0 downloads0 reach0 impact
The performanceEstimation standardWF using svm as the learner
59 runs0 likes1 downloads1 reach0 impact
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
59 runs0 likes1 downloads1 reach0 impact
G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
59 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.cforest.preproc from package(s) party.
59 runs0 likes0 downloads0 reach0 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
59 runs0 likes2 downloads2 reach54 impact
Learner mlr.classif.multinom.preproc from package(s) nnet.
58 runs0 likes0 downloads0 reach0 impact
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
58 runs0 likes1 downloads1 reach0 impact
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
58 runs0 likes1 downloads1 reach0 impact
Automatically created sub-component.
58 runs0 likes0 downloads0 reach57 impact
Learner mlr.classif.C50.preproc from package(s) C50.
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.earth.preproc from package(s) !earth.
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.featureless.preproc from package(s) mlr.
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.kknn.preproc from package(s) !kknn.
57 runs0 likes0 downloads0 reach0 impact
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
57 runs0 likes1 downloads1 reach0 impact
Learner classif.extraTrees from package(s) extraTrees.
57 runs0 likes1 downloads1 reach0 impact
Learner classif.hdrda from package(s) sparsediscrim.
57 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
57 runs0 likes0 downloads0 reach0 impact
Weka implementation of DecisionStump
57 runs0 likes3 downloads3 reach2 impact
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
57 runs0 likes2 downloads2 reach50 impact
Learner mlr.classif.ctree.preproc from package(s) party.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.gbm.preproc from package(s) gbm.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.glmnet.preproc from package(s) glmnet.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.rpart.preproc from package(s) rpart.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.svm.preproc from package(s) e1071.
56 runs0 likes0 downloads0 reach0 impact
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
56 runs0 likes1 downloads1 reach0 impact
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
56 runs0 likes2 downloads2 reach51 impact
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
56 runs0 likes4 downloads4 reach48 impact
Learner mlr.classif.rda.preproc from package(s) klaR.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.ksvm.preproc from package(s) kernlab.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.cvglmnet.preproc from package(s) glmnet.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.evtree.preproc from package(s) evtree.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.gausspr.preproc from package(s) kernlab.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.nnet.preproc from package(s) nnet.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.randomForest.preproc from package(s) randomForest.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.RRF.preproc from package(s) RRF.
55 runs0 likes0 downloads0 reach0 impact
Weka implementation of FilteredClassifier
55 runs0 likes1 downloads1 reach0 impact
Flow generated by openml_run
55 runs0 likes5 downloads5 reach17 impact
Moa implementation of NaiveBayesMultinomial
55 runs0 likes2 downloads2 reach47 impact
Moa implementation of StackingAttemptV2
55 runs0 likes2 downloads2 reach46 impact
Weka implementation of MultiClassClassifierUpdateable
55 runs0 likes1 downloads1 reach0 impact
Weka implementation of MultiScheme
55 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.mda.preproc from package(s) !mda.
55 runs0 likes0 downloads0 reach0 impact
Weka implementation of RandomCommittee
54 runs0 likes1 downloads1 reach0 impact
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
54 runs0 likes1 downloads1 reach0 impact
Moa implementation of StackingAttempt
54 runs0 likes2 downloads2 reach46 impact
Weka implementation of CostSensitiveClassifier
53 runs0 likes0 downloads0 reach0 impact
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
53 runs0 likes1 downloads1 reach0 impact
Moa implementation of StackingAttempt
53 runs0 likes2 downloads2 reach44 impact
Moa implementation of StackingAttemptV2
53 runs0 likes2 downloads2 reach45 impact
Learner mlr.classif.glmnet from package(s) glmnet.
53 runs0 likes0 downloads0 reach0 impact