OpenML
Cortana plugin measure
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Cortana plugin measure
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Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence,…
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Weka implementation of MultiSearch
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Weka implementation of RandomSearch
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Weka implementation of MultiSearch
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Weka implementation of MultiSearch
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Weka implementation of DefaultSearch
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Learner mlr.classif.J48 from package(s) RWeka.
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Learner mlr.classif.ctree from package(s) party.
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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A RapidMiner Operator
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.J48 from package(s) RWeka.
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Learner mlr.classif.ctree from package(s) party.
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Learner mlr.classif.J48 from package(s) RWeka.
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Learner mlr.classif.ctree from package(s) party.
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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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.rpart.preproc.filtered from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.svm.preproc.preproc from package(s) e1071.
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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.rpart.preproc.preproc from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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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.glmnet.preproc.preproc from package(s) glmnet.
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Learner mlr.classif.glmnet.preproc from package(s) glmnet.
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Learner mlr.classif.glmnet from package(s) glmnet.
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Learner mlr.classif.gbm.preproc.preproc from package(s) gbm.
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Learner mlr.classif.gbm.preproc from package(s) gbm.
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Learner mlr.classif.gbm from package(s) gbm.
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Learner mlr.classif.ranger.preproc.preproc from package(s) ranger.
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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.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.C50 from package(s) C50.
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Learner mlr.classif.C50 from package(s) C50.
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Learner mlr.classif.C50 from package(s) C50.
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Learner mlr.classif.C50 from package(s) C50.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.C50 from package(s) C50.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Learner mlr.classif.rpart from package(s) rpart.
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Moa implementation of WEKAClassifier
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Weka implementation of MultiSearch
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Weka implementation of MultilayerPerceptron
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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Automatically created sub-component.
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Automatically created sub-component.
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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. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of IterativeClassifierOptimizer
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Weka implementation of CostSensitiveClassifier
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Weka implementation of CostSensitiveClassifier
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Weka implementation of CostSensitiveClassifier
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Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud.. 55(3):281-292.
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Weka implementation of CostSensitiveClassifier
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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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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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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David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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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. 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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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…
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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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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of IterativeClassifierOptimizer
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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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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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.
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Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
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