OpenML
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of MultiClassClassifierUpdateable
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Weka implementation of SGD
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Weka implementation of CostSensitiveClassifier
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Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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Weka implementation of MultiClassClassifier
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Weka implementation of MultiClassClassifier
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Weka implementation of SimpleLinearRegression
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E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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Weka implementation of MultiClassClassifier
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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…
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E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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Weka implementation of IterativeClassifierOptimizer
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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Weka implementation of FilteredClassifier
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Weka implementation of CostSensitiveClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of RandomTree
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Weka implementation of CostSensitiveClassifier
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Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
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Weka implementation of FilteredClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of CostSensitiveClassifier
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of CostSensitiveClassifier
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Learner mlr.weightedclasses.classif.rpart.preproc.overbagged from package(s) rpart.
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E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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Weka implementation of RandomCommittee
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Learner mlr.weightedclasses.classif.gbm.preproc.overbagged from package(s) gbm.
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Weka implementation of FilteredClassifier
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Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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Weka implementation of CostSensitiveClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of FilteredClassifier
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Learner mlr.weightedclasses.classif.xgboost.preproc.overbagged from package(s) xgboost.
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Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
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Weka implementation of CostSensitiveClassifier
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of WeightedInstancesHandlerWrapper
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Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
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Weka implementation of CostSensitiveClassifier
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Learner mlr.weightedclasses.classif.xgboost.preproc.smoted from package(s) xgboost.
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Weka implementation of CostSensitiveClassifier
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Weka implementation of MultiSearch
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Weka implementation of FilteredClassifier
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Weka implementation of CostSensitiveClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of CostSensitiveClassifier
3 runs0 likes0 downloads0 reach0 impact
Weka implementation of FilteredClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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Weka implementation of MultiSearch
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Weka implementation of CostSensitiveClassifier
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of FilteredClassifier
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Weka implementation of CostSensitiveClassifier
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Weka implementation of CostSensitiveClassifier
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/95fb76a6c6c645dc5915754b739f2c00b12df542/rstuff/openml_ctree.R and devel partykit package revision 1034:…
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
0 runs0 likes0 downloads0 reach0 impact
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/95fb76a6c6c645dc5915754b739f2c00b12df542/rstuff/openml_ctree.R and devel partykit package revision 1034:…
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Flow generated by run_task
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Moa implementation of OzaBag
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Moa implementation of LeveragingBag
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Flow generated by run_task
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Flow generated by run_task
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Please use the mlr add-on code and devel partykit package revision 1034:
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Please use the mlr add-on code and devel partykit package revision 1078: https://r-forge.r-project.org/scm/viewvc.php/pkg/devel/?root=partykit
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Please use the mlr add-on code and devel partykit package revision 1078: https://r-forge.r-project.org/scm/viewvc.php/pkg/devel/?root=partykit
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Learner mlr.classif.xgboost from package(s) xgboost.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Please use the mlr add-on code and devel partykit package revision 1078: https://r-forge.r-project.org/scm/viewvc.php/pkg/devel/?root=partykit
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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 1082:…
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Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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Learner mlr.classif.rpart.tuned from package(s) rpart.
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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 1089:…
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