Flow
ID's
Sort: fewest runs
20895 results
mlr.classif.rpart.preproc (13)
Learner mlr.classif.rpart.preproc from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc.filtered (9)
Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc (14)
Learner mlr.classif.rpart.preproc from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm.preproc.preproc (3)
Learner mlr.classif.svm.preproc.preproc from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm.preproc (3)
Learner mlr.classif.svm.preproc from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm (4)
Learner mlr.classif.svm from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc.preproc (4)
Learner mlr.classif.rpart.preproc.preproc from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc (15)
Learner mlr.classif.rpart.preproc from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn.preproc.preproc (10)
Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn.preproc (6)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn (7)
Learner mlr.classif.kknn from package(s) !kknn.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet.preproc.preproc (1)
Learner mlr.classif.glmnet.preproc.preproc from package(s) glmnet.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet.preproc (1)
Learner mlr.classif.glmnet.preproc from package(s) glmnet.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (1)
Learner mlr.classif.glmnet from package(s) glmnet.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.gbm.preproc.preproc (2)
Learner mlr.classif.gbm.preproc.preproc from package(s) gbm.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.gbm.preproc (2)
Learner mlr.classif.gbm.preproc from package(s) gbm.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.gbm (2)
Learner mlr.classif.gbm from package(s) gbm.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger.preproc.preproc (2)
Learner mlr.classif.ranger.preproc.preproc from package(s) ranger.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger.preproc (2)
Learner mlr.classif.ranger.preproc from package(s) ranger.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (6)
Learner mlr.classif.ranger from package(s) ranger.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.naiveBayes.preproc (5)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.naiveBayes (5)
Learner mlr.classif.naiveBayes from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (1)
Learner mlr.classif.C50 from package(s) C50.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (3)
Learner mlr.classif.C50 from package(s) C50.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (5)
Learner mlr.classif.C50 from package(s) C50.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (8)
Learner mlr.classif.C50 from package(s) C50.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (20)
Learner mlr.classif.rpart from package(s) rpart.
0 runs0 likes1 downloads1 reach0 impact
mlr.classif.C50 (10)
Learner mlr.classif.C50 from package(s) C50.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.naiveBayes (6)
Learner mlr.classif.naiveBayes from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.naiveBayes (7)
Learner mlr.classif.naiveBayes from package(s) e1071.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (22)
Learner mlr.classif.rpart from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
moa.WEKAClassifier (2)
Moa implementation of WEKAClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_MultilayerPerceptron (2)
Weka implementation of MultiSearch
0 runs0 likes0 downloads0 reach0 impact
weka.MultilayerPerceptron (6)
Weka implementation of MultilayerPerceptron
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn (8)
Learner mlr.classif.kknn from package(s) !kknn.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.imputed.filtered (1)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.imputed.filtered (2)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.imputation.Imputer (2)
Automatically created sub-component.
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.OneHotEncoder (2)
Automatically created sub-component.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_NaiveBayes (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_DecisionStump (9)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.IterativeClassifierOptimizer_AdditiveRegression_DecisionStump (2)
Weka implementation of IterativeClassifierOptimizer
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_M5P (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_GaussianProcesses_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_LinearRegression (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.UserClassifier (1)
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.
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_SMO_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.AdditiveRegression_LMT (1)
J.H. Friedman (1999). Stochastic Gradient Boosting.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_LMT (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.Stacking_AdaBoostM1 (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_Stacking_LogitBoost (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.Stacking_LogitBoost (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_RandomForest (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_LogitBoost_J48 (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_J48 (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LMT (5)
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…
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_RandomForest (11)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_LogitBoost_RandomTree (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_RandomTree (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump (3)
Weka implementation of IterativeClassifierOptimizer
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging (3)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_J48 (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
0 runs0 likes0 downloads0 reach0 impact
weka.KStar (5)
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.
0 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomial (5)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_DecisionTable (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.M5P (5)
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…
0 runs0 likes0 downloads0 reach0 impact
weka.DecisionTable (2)
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
0 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_PART (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.PART (5)
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_JRip (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.JRip (6)
William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
0 runs0 likes0 downloads0 reach0 impact
weka.RandomCommittee_Bagging_RandomTree (1)
Weka implementation of RandomCommittee
0 runs0 likes1 downloads1 reach0 impact
weka.NaiveBayesMultinomialUpdateable (8)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_J48 (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_HoeffdingTree (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_LMT (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_M5P (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_LinearRegression (2)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.LinearRegression (8)
Weka implementation of LinearRegression
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_GaussianProcesses_PolyKernel (2)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.GaussianProcesses_PolyKernel (10)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
0 runs0 likes0 downloads0 reach0 impact
weka.PolyKernel (10)
Weka implementation of PolyKernel
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_SerializedClassifier (1)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.SerializedClassifier (1)
Weka implementation of SerializedClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.InputMappedClassifier_ZeroR (2)
Weka implementation of InputMappedClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.Logistic (7)
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_NaiveBayesMultinomial (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_NaiveBayesMultinomialUpdateable (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.SGD (5)
Weka implementation of SGD
0 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifier_SimpleLinearRegression (1)
Weka implementation of MultiClassClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.SimpleLinearRegression (5)
Weka implementation of SimpleLinearRegression
0 runs0 likes0 downloads0 reach0 impact
weka.LWL_RandomForest (3)
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…
0 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_NaiveBayes (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
0 runs0 likes0 downloads0 reach0 impact
weka.IterativeClassifierOptimizer_AdditiveRegression_DecisionStump (3)
Weka implementation of IterativeClassifierOptimizer
0 runs0 likes0 downloads0 reach0 impact
weka.AdditiveRegression_DecisionStump (5)
J.H. Friedman (1999). Stochastic Gradient Boosting.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_RandomTree (12)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.RandomTree (13)
Weka implementation of RandomTree
0 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_RandomTree (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_Bagging_DecisionStump (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact