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 fit…
68 runs0 likes0 downloads0 reach0 impact
Learner classif.lda from package(s) MASS.
67 runs0 likes2 downloads2 reach5 impact
Learner classif.pamr from package(s) pamr.
67 runs0 likes1 downloads1 reach0 impact
Learner classif.gbm from package(s) gbm.
67 runs0 likes1 downloads1 reach0 impact
Automatically created scikit-learn flow.
67 runs0 likes0 downloads0 reach67 impact
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,…
67 runs0 likes0 downloads0 reach0 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
66 runs0 likes2 downloads2 reach58 impact
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
66 runs0 likes2 downloads2 reach52 impact
Learner mlr.classif.ksvm.preproc from package(s) kernlab.
66 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.glmnet.preproc from package(s) glmnet.
66 runs0 likes0 downloads0 reach0 impact
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 fit…
66 runs0 likes0 downloads0 reach0 impact
Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on…
66 runs0 likes0 downloads0 reach0 impact
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 fit…
66 runs0 likes0 downloads0 reach0 impact
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 fit…
66 runs0 likes0 downloads0 reach0 impact
Learner classif.JRip from package(s) RWeka.
65 runs0 likes1 downloads1 reach0 impact
Learner classif.J48 from package(s) RWeka.
65 runs0 likes1 downloads1 reach0 impact
Learner classif.plr from package(s) stepPlr.
65 runs0 likes1 downloads1 reach0 impact
Moa implementation of kNNwithPAW
65 runs0 likes2 downloads2 reach45 impact
Moa implementation of ASHoeffdingTree
65 runs0 likes2 downloads2 reach33 impact
Learner classif.boosting from package(s) adabag, rpart.
65 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.evtree.preproc from package(s) evtree.
65 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
Moa implementation of DynamicWeightedMajority
64 runs0 likes0 downloads0 reach0 impact
Automatically created scikit-learn flow.
64 runs0 likes0 downloads0 reach0 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
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
Automatically created scikit-learn flow.
63 runs0 likes0 downloads0 reach58 impact
Weka implementation.
63 runs0 likes0 downloads0 reach0 impact
Weka implementation of ZeroR
63 runs0 likes4 downloads4 reach338 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
Moa implementation of RuleClassifier
62 runs0 likes0 downloads0 reach0 impact
Moa implementation of NoChange
62 runs0 likes0 downloads0 reach0 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.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
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 runs0 likes0 downloads0 reach0 impact
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 fit…
62 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
Learner mlr.classif.lda.preproc from package(s) MASS.
61 runs0 likes0 downloads0 reach0 impact
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 fit…
61 runs0 likes0 downloads0 reach14 impact
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 fit…
61 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
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
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
60 runs0 likes2 downloads2 reach49 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
Automatically created scikit-learn flow.
60 runs0 likes0 downloads0 reach0 impact
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 fit…
60 runs0 likes0 downloads0 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
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
59 runs0 likes2 downloads2 reach54 impact
The performanceEstimation standardWF using svm as the learner
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.gausspr.preproc from package(s) kernlab.
59 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.cforest.preproc from package(s) party.
59 runs0 likes0 downloads0 reach0 impact
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 fit…
59 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.multinom.preproc from package(s) nnet.
58 runs0 likes0 downloads0 reach0 impact
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
58 runs0 likes0 downloads0 reach0 impact
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 fit…
58 runs0 likes0 downloads0 reach50 impact
This class implements the regression task.
58 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
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 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.C50.preproc from package(s) C50.
57 runs0 likes0 downloads0 reach0 impact