OpenML
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…
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Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
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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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Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
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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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William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
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Weka implementation of RandomCommittee
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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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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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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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Weka implementation of LinearRegression
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Weka implementation of CostSensitiveClassifier
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David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
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Weka implementation of PolyKernel
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Weka implementation of CostSensitiveClassifier
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Weka implementation of SerializedClassifier
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Weka implementation of InputMappedClassifier
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le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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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 SGD
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Weka implementation of MultiClassClassifier
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Weka implementation of SimpleLinearRegression
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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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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of RandomTree
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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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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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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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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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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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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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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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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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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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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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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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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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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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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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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:…
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Automatically created sub-component.
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Learner mlr.classif.C50.preproc from package(s) C50.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created sub-component.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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