Flow
Learner mlr.classif.rpart.preproc.filtered from package(s) randomForestSRC, rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner classif.rpart from package(s) rpart.
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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Weka implementation of LinearRegression
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Learner mlr.classif.logreg.preproc.filtered from package(s) stats.
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Learner mlr.classif.logreg.preproc from package(s) stats.
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Learner mlr.classif.knn from package(s) class.
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Learner mlr.classif.knn.preproc from package(s) class.
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Learner mlr.classif.ctree from package(s) party.
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Learner mlr.classif.rpart from package(s) rpart.
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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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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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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,…
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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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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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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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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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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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R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
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Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005. Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for…
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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 RandomCommittee
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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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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Weka implementation of MultiSearch
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Weka implementation of MultiSearch
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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,…
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Weka implementation of MultiSearch
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Weka implementation of MultiSearch
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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Weka implementation of MultiSearch
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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 MultiSearch
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Weka implementation of MultiSearch
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Weka implementation of MultiSearch
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.mlp.preproc from package(s) RSNNS.
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Learner mlr.classif.mlp from package(s) RSNNS.
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Learner mlr.classif.PART.preproc from package(s) RWeka.
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Learner mlr.classif.PART from package(s) RWeka.
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Learner mlr.classif.svm.preproc from package(s) e1071.
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Learner mlr.classif.svm from package(s) e1071.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.mlp.preproc from package(s) RSNNS.
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Learner mlr.classif.mlp from package(s) RSNNS.
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Learner mlr.classif.PART.preproc from package(s) RWeka.
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Learner mlr.classif.PART from package(s) RWeka.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.mlp.preproc from package(s) RSNNS.
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Learner mlr.classif.mlp from package(s) RSNNS.
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Learner mlr.classif.logreg.preproc from package(s) stats.
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Learner mlr.classif.logreg from package(s) stats.
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Learner mlr.classif.PART.preproc from package(s) RWeka.
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Learner mlr.classif.svm.preproc from package(s) e1071.
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Learner mlr.classif.PART from package(s) RWeka.
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Learner mlr.classif.svm from package(s) e1071.
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Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Learner mlr.classif.fnn.preproc from package(s) FNN.
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Learner mlr.classif.nnet.preproc from package(s) nnet.
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Learner mlr.classif.nnet from package(s) nnet.
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Learner mlr.classif.fnn from package(s) FNN.
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Learner mlr.classif.ada.preproc from package(s) ada.
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Learner mlr.classif.boosting.preproc from package(s) adabag, rpart.
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Learner mlr.classif.ada from package(s) ada.
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Learner mlr.classif.boosting from package(s) adabag, rpart.
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Learner mlr.classif.ranger.preproc from package(s) ranger.
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Learner mlr.classif.ranger from package(s) ranger.
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Learner mlr.classif.JRip.preproc from package(s) RWeka.
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Learner mlr.classif.JRip from package(s) RWeka.
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Learner mlr.classif.bdk.preproc from package(s) kohonen.
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Learner mlr.classif.bdk from package(s) kohonen.
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Learner mlr.classif.saeDNN.preproc from package(s) deepnet.
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Learner mlr.classif.dbnDNN.preproc from package(s) deepnet.
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